Clustering is a pivotal data analysis method for deciphering the charge transport properties of single molecules in break junction experiments.However,given the high dimensionality and variability of the data,feature ...Clustering is a pivotal data analysis method for deciphering the charge transport properties of single molecules in break junction experiments.However,given the high dimensionality and variability of the data,feature extraction remains a bottleneck in the development of efficient clustering methods.In this regard,extensive research over the past two decades has focused on feature engineering and dimensionality reduction in break junction conductance.However,extracting highly relevant features without expert knowledge remains an unresolved challenge.To address this issue,we propose a deep clustering method driven by task-oriented representation learning(CTRL)in which the clustering module serves as a guide for the representation learning(RepL)module.First,we determine an optimal autoencoder(AE)structure through a neural architecture search(NAS)to ensure efficient RepL;second,the RepL process is guided by a joint training strategy that combines AE reconstruction loss with the clustering objective.The results demonstrate that CTRL achieves excellent performance on both the generated and experimental data.Further inspection of the RepL step reveals that joint training robustly learns more compact features than the unconstrained AE or traditional dimensionality reduction methods,significantly reducing misclustering possibilities.Our method provides a general end-to-end automatic clustering solution for analyzing single-molecule break junction data.展开更多
Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insigh...Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effective MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models’generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.展开更多
Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all cha...Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.展开更多
Learning discriminative representations with deep neural networks often relies on massive labeled data, which is expensive and difficult to obtain in many real scenarios. As an alternative, self-supervised learning th...Learning discriminative representations with deep neural networks often relies on massive labeled data, which is expensive and difficult to obtain in many real scenarios. As an alternative, self-supervised learning that leverages input itself as supervision is strongly preferred for its soaring performance on visual representation learning. This paper introduces a contrastive self-supervised framework for learning generalizable representations on the synthetic data that can be obtained easily with complete controllability.Specifically, we propose to optimize a contrastive learning task and a physical property prediction task simultaneously. Given the synthetic scene, the first task aims to maximize agreement between a pair of synthetic images generated by our proposed view sampling module, while the second task aims to predict three physical property maps, i.e., depth, instance contour maps, and surface normal maps. In addition, a feature-level domain adaptation technique with adversarial training is applied to reduce the domain difference between the realistic and the synthetic data. Experiments demonstrate that our proposed method achieves state-of-the-art performance on several visual recognition datasets.展开更多
The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system f...The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network.In order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed.Firstly,the route network of a multi-airport system with attribute information is constructed.If there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport node.Secondly,the airport attributes and the airport network vector are represented respectively.The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is obtained.By calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system.The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.展开更多
A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chin...A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.展开更多
Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based o...Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based on graph convolutional network(GCN).Methods Clauses that contain symptoms,formulas,and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs,which were used to propose a node representation learning method based on GCN−the Traditional Chinese Medicine Graph Convolution Network(TCM-GCN).The symptom-formula,symptom-herb,and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes,and thus acquiring the nodes’sum-aggregations of symptoms,formulas,and herbs to lay a foundation for the downstream tasks of the prediction models.Results Comparisons among the node representations with multi-hot encoding,non-fusion encoding,and fusion encoding showed that the Precision@10,Recall@10,and F1-score@10 of the fusion encoding were 9.77%,6.65%,and 8.30%,respectively,higher than those of the non-fusion encoding in the prediction studies of the model.Conclusion Node representations by fusion encoding achieved comparatively ideal results,indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.展开更多
The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of netw...The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of network structure,diversity of network nodes,and sparsity of data all pose difficulties in predicting propagation.This paper proposes a malware propagation prediction model based on representation learning and Graph Convolutional Networks(GCN)to address the aforementioned problems.First,to solve the problem of the inaccuracy of infection intensity calculation caused by the sparsity of node interaction behavior data in the malware propagation network,a mechanism based on a tensor to mine the infection intensity among nodes is proposed to retain the network structure information.The influence of the relationship between nodes on the infection intensity is also analyzed.Second,given the diversity and complexity of the content and structure of infected and normal nodes in the network,considering the advantages of representation learning in data feature extraction,the corresponding representation learning method is adopted for the characteristics of infection intensity among nodes.This can efficiently calculate the relationship between entities and relationships in low dimensional space to achieve the goal of low dimensional,dense,and real-valued representation learning for the characteristics of propagation spatial data.We also design a new method,Tensor2vec,to learn the potential structural features of malware propagation.Finally,considering the convolution ability of GCN for non-Euclidean data,we propose a dynamic prediction model of malware propagation based on representation learning and GCN to solve the time effectiveness problem of the malware propagation carrier.The experimental results show that the proposed model can effectively predict the behaviors of the nodes in the network and discover the influence of different characteristics of nodes on the malware propagation situation.展开更多
There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of netwo...There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.展开更多
With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the rou...With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.展开更多
With the wide application of location-based social networks(LBSNs),personalized point of interest(POI)recommendation becomes popular,especially in the commercial field.Unfortunately,it is challenging to accurately rec...With the wide application of location-based social networks(LBSNs),personalized point of interest(POI)recommendation becomes popular,especially in the commercial field.Unfortunately,it is challenging to accurately recommend POIs to users because the user-POI matrix is extremely sparse.In addition,a user's check-in activities are affected by many influential factors.However,most of existing studies capture only few influential factors.It is hard for them to be extended to incorporate other heterogeneous information in a unified way.To address these problems,we propose a meta-path-based deep representation learning(MPDRL)model for personalized POI recommendation.In this model,we design eight types of meta-paths to fully utilize the rich heterogeneous information in LBSNs for the representations of users and POIs,and deeply mine the correlations between users and POIs.To further improve the recommendation performance,we design an attention-based long short-term memory(LSTM)network to learn the importance of different influential factors on a user's specific check-in activity.To verify the effectiveness of our proposed method,we conduct extensive experiments on a real-world dataset,Foursquare.Experimental results show that the MPDRL model improves at least 16.97%and 23.55%over all comparison methods in terms of the metric Precision@N(Pre@N)and Recall@N(Rec@N)respectively.展开更多
In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owin...In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart performance.Yet,these models fail to consider the classification error while learning the feature representation.Intuitively,the learnt feature representation may degrade the performance of the classification task.For the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion detection.Specially,the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously.This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection.Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model.First,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed model.Next,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed model.Finally,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.展开更多
Satellite communication provides crucial connectivity for low-altitude aircraft to address coverage gaps in groundbased internet and aerial communication blind spots.Achieving this capability requires robust anomaly d...Satellite communication provides crucial connectivity for low-altitude aircraft to address coverage gaps in groundbased internet and aerial communication blind spots.Achieving this capability requires robust anomaly detection methods.However,existing approaches struggle to capture the complex spatio-temporal relationships in space-air communications due to challenges such as unpredictable network topology from real-time route adjustments,the absence of predefined communication link patterns,and rapid spatio-temporal relationship evolution among moving nodes.To address these challenges,this paper proposes a graph representation anomaly detection framework tailored for communication networks between low Earth orbit(LEO)satellite constellations and low-altitude aircraft.The spatio-temporal relationships between satellites and aircraft are modeled using dynamic graph structures,which capture 3D locations of each node and anomaly characteristics of each link.The proposed framework is compatible with diverse anomaly detection learning algorithms,and several static and dynamic detection algorithms are evaluated and compared in this work.Furthermore,authors present an improved variant of a novel transformer-based anomaly detection framework for dynamic graphs(TADDY),which involves temporal periodic encoding,semi-supervised comparative learning,and multiscale graph attention mechanisms.We evaluate the framework through simulated scenarios,comparing Walker and broken-chain constellations with varying air traffic densities in a specific region.The anomaly detection performances at different anomaly ratios and air traffic densities are evaluated.Experimental results demonstrate that,the performances of static anomaly detection methods and TADDY algorithm degrade significantly as air traffic density increases.Meanwhile,the proposed improved TADDY achieves an average AUC of 0.95 for Walker constellation and 0.86 for the broken-chain constellation,outperforming the original TADDY in both accuracy and reliability under high anomaly rates.Finally,sensitivity analysis and ablation studies confirm the framework's high responsiveness to anomalies such as abrupt topological changes,offering an efficient solution for ensuring the reliability of large-scale satellite-aircraft communication systems.展开更多
The escalating energy crisis has spurred extensive research into organic compounds for energyefficient applications,taking advantage of their environmental friendliness,cost-effective synthesis,and adaptable molecular...The escalating energy crisis has spurred extensive research into organic compounds for energyefficient applications,taking advantage of their environmental friendliness,cost-effective synthesis,and adaptable molecular structures.Traditional trial-and-error methods for discovering highly functional organic compounds are expensive and time-consuming.We employed a 3D transformerbased molecular representation learning algorithm to create the Org-Mol pre-trained model,using 60 million semi-empirically optimized small organic molecule structures.After fine-tuning with public experimental data,the model can accurately predict various physical properties of pure organics,with test set R2 values exceeding 0.92.These fine-tuned models are used in high-throughput screening among millions of ester molecules to identify novel immersion coolants,resulting in the experimental validation of two promising candidates.This work not only demonstrates the potential of Org-Mol in predicting bulk properties for pure organic compounds but also paves the way for the rational and efficient development of ideal candidates for energy-saving materials.展开更多
The effectiveness of machine learning(ML)models for architectural applications relies on high-quality datasets balanced with advancements in model architecture and computational capacity.Current methods for evaluating...The effectiveness of machine learning(ML)models for architectural applications relies on high-quality datasets balanced with advancements in model architecture and computational capacity.Current methods for evaluating architectural floor plan datasets typically depend on explicit semantic annotations,which limit their effectiveness and scalability when annotations are unavailable or inconsistent.To address this limitation,this research develops an isovist-based latent representation approach to quantitatively measure typicality and diversity within architectural datasets without relying on semantic labels.We introduce Isovist Latent Norm Typicality,a metric that leverages the statistical structure of latent representations derived from isovist morphological features using a variational autoencoder(VAE).This metric quantifies typicality by analyzing distributional shifts in latent representations between individual floor plans and a reference dataset using a modified Wasserstein distance.Experimental results demonstrate the approach’s ability to distinguish typical from atypical floor plan configurations,capturing the morphological features that complement conventional metrics.Comparative analysis indicates that our method provides insights into spatial organization,correlating with conventional properties such as programmatic diversity and spatial openness.By quantifying typicality through purely morphological features,the proposed methodology facilitates dataset curation prior to costly semantic annotation,enhancing dataset quality and enabling scalability to more extensive and diverse architectural datasets.展开更多
The rational design of organic functional devices relies on understanding structure-propertyperformance relationships through multi-scale characterization.However,traditional characterizations are costly and require m...The rational design of organic functional devices relies on understanding structure-propertyperformance relationships through multi-scale characterization.However,traditional characterizations are costly and require multidisciplinary expertise.Here we present OCNet,a domain-knowledge-enhanced representation learning framework that,for the first time,enables unified virtual characterization from molecules to devices.Pre-trained on over ten million selfgenerated conjugated molecules and dimers,OCNet learns generalizable microscopic representations comparable to expert-crafted features.As a result,it surpasses state-of-the-art models by over 20%in predicting key computed and experimental molecular optoelectronic properties.OCNet further provides the first transferable model for predicting transfer integrals in thin films,enabling accurate mesoscale carrier mobility estimation via multiscale simulations.By integrating tight-binding-level electronic descriptors,OCNet achieves near real-time,accurate prediction of device power conversion efficiency.Together,OCNet offers a unified and scalable foundation for virtual characterization of organic materials across multiple scales,with broad applicability in photovoltaics,displays,and sensing.展开更多
With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Curren...With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Current trajectory data processing methods typically require input in the form of fixed-length vectors,making it crucial to convert variable-length trajectory data into fixed-length,low-dimensional embedding vectors.Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations.This paper provides a comprehensive review of the research progress,methodologies,and applications of trajectory representation learning.First,it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets.Then,it classifies trajectory representation learning methods based on various downstream tasks,with a focus on their principles,advantages,limitations,and application scenarios in trajectory similarity computation,similar trajectory search,trajectory clustering,and trajectory prediction.Additionally,representative model structures and principles in each task are analyzed,along with the characteristics and advantages of different methods in each task.Last,the challenges faced by current trajectory representation learning methods are analyzed,including data sparsity,multimodality,model optimization,and privacy protection,while potential research directions and methodologies to address these challenges are explored.展开更多
Entity alignment,which aims to identify entities with the same meaning in different Knowledge Graphs(KGs),is a key step in knowledge integration.Despite the promising results achieved by existing methods,they often fa...Entity alignment,which aims to identify entities with the same meaning in different Knowledge Graphs(KGs),is a key step in knowledge integration.Despite the promising results achieved by existing methods,they often fail to fully leverage the structure information of KGs for entity alignment.Therefore,our goal is to thoroughly explore the features of entity neighbors and relationships to obtain better entity embeddings.In this work,we propose DCEA,an effective dual-context representation learning framework for entity alignment.Specifically,the neighbor-level embedding module introduces relation information to more accurately aggregate neighbor context.The relation-level embedding module utilizes neighbor context to enhance relation-level embeddings.To eliminate semantic gaps between neighbor-level and relation-level embeddings,and fully exploit their complementarity,we design a hybrid embedding fusion model that adaptively performs embedding fusion to obtain powerful joint entity embeddings.We also jointly optimize the contrastive loss of multi-level embeddings,enhancing their mutual reinforcement while preserving the characteristics of neighbor and relation embeddings.Additionally,the decision fusion module combines the similarity scores calculated between entities based on embeddings at different levels to make the final alignment decision.Extensive experimental results on public datasets indicate that our DCEA performs better than state-of-the-art baselines.展开更多
Predicting mortality risk in the Intensive Care Unit(ICU)using Electronic Medical Records(EMR)is crucial for identifying patients in need of immediate attention.However,the incompleteness and the variability of EMR fe...Predicting mortality risk in the Intensive Care Unit(ICU)using Electronic Medical Records(EMR)is crucial for identifying patients in need of immediate attention.However,the incompleteness and the variability of EMR features for each patient make mortality prediction challenging.This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges.The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data,enabling the effective and explainable fusion of the incomplete features.Modality-specific encoders are employed to encode each modality feature separately.To tackle the variability and incompleteness of input features among patients,a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs.Furthermore,a MultiModal Gated Contrastive Representation Learning(MMGCRL)method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance.We evaluate the proposed framework using the large-scale ICU dataset,MIMIC-III.Experimental results demonstrate its effectiveness in mortality prediction,outperforming several state-of-the-art methods.展开更多
基金supported by Guangxi Science and Technology Program(No.GuiKeAD23026291)Guangxi Science and Technology Major Project(No.AA22068057).
文摘Clustering is a pivotal data analysis method for deciphering the charge transport properties of single molecules in break junction experiments.However,given the high dimensionality and variability of the data,feature extraction remains a bottleneck in the development of efficient clustering methods.In this regard,extensive research over the past two decades has focused on feature engineering and dimensionality reduction in break junction conductance.However,extracting highly relevant features without expert knowledge remains an unresolved challenge.To address this issue,we propose a deep clustering method driven by task-oriented representation learning(CTRL)in which the clustering module serves as a guide for the representation learning(RepL)module.First,we determine an optimal autoencoder(AE)structure through a neural architecture search(NAS)to ensure efficient RepL;second,the RepL process is guided by a joint training strategy that combines AE reconstruction loss with the clustering objective.The results demonstrate that CTRL achieves excellent performance on both the generated and experimental data.Further inspection of the RepL step reveals that joint training robustly learns more compact features than the unconstrained AE or traditional dimensionality reduction methods,significantly reducing misclustering possibilities.Our method provides a general end-to-end automatic clustering solution for analyzing single-molecule break junction data.
基金supported by the Natural Science Foundation of Wenzhou University of Technology,China(Grant No.:ky202211).
文摘Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effective MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models’generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.
基金Projects(11661069,61763041) supported by the National Natural Science Foundation of ChinaProject(IRT_15R40) supported by Changjiang Scholars and Innovative Research Team in University,ChinaProject(2017TS045) supported by the Fundamental Research Funds for the Central Universities,China
文摘Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.
基金by National Natural Science Foundation of China(Nos.61822204 and 61521002).
文摘Learning discriminative representations with deep neural networks often relies on massive labeled data, which is expensive and difficult to obtain in many real scenarios. As an alternative, self-supervised learning that leverages input itself as supervision is strongly preferred for its soaring performance on visual representation learning. This paper introduces a contrastive self-supervised framework for learning generalizable representations on the synthetic data that can be obtained easily with complete controllability.Specifically, we propose to optimize a contrastive learning task and a physical property prediction task simultaneously. Given the synthetic scene, the first task aims to maximize agreement between a pair of synthetic images generated by our proposed view sampling module, while the second task aims to predict three physical property maps, i.e., depth, instance contour maps, and surface normal maps. In addition, a feature-level domain adaptation technique with adversarial training is applied to reduce the domain difference between the realistic and the synthetic data. Experiments demonstrate that our proposed method achieves state-of-the-art performance on several visual recognition datasets.
基金supported by the Natural Science Foundation of Tianjin(No.20JCQNJC00720)the Fundamental Research Fund for the Central Universities(No.3122021052)。
文摘The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network.In order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed.Firstly,the route network of a multi-airport system with attribute information is constructed.If there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport node.Secondly,the airport attributes and the airport network vector are represented respectively.The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is obtained.By calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system.The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.
基金Supported by the National Natural Science Foundation of China(No.61303179,U1135005,61175020)
文摘A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.
基金New-Generation Artificial Intelligence-Major Program in the Sci-Tech Innovation 2030 Agenda from the Ministry of Science and Technology of China(2018AAA0102100)Hunan Provincial Department of Education key project(21A0250)The First Class Discipline Open Fund of Hunan University of Traditional Chinese Medicine(2022ZYX08)。
文摘Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based on graph convolutional network(GCN).Methods Clauses that contain symptoms,formulas,and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs,which were used to propose a node representation learning method based on GCN−the Traditional Chinese Medicine Graph Convolution Network(TCM-GCN).The symptom-formula,symptom-herb,and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes,and thus acquiring the nodes’sum-aggregations of symptoms,formulas,and herbs to lay a foundation for the downstream tasks of the prediction models.Results Comparisons among the node representations with multi-hot encoding,non-fusion encoding,and fusion encoding showed that the Precision@10,Recall@10,and F1-score@10 of the fusion encoding were 9.77%,6.65%,and 8.30%,respectively,higher than those of the non-fusion encoding in the prediction studies of the model.Conclusion Node representations by fusion encoding achieved comparatively ideal results,indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.
基金This research is partially supported by the National Natural Science Foundation of China(Grant No.61772098)Chongqing Technology Innovation and Application Development Project(Grant No.cstc2020jscxmsxmX0150)+2 种基金Chongqing Science and Technology Innovation Leading Talent Support Program(CSTCCXLJRC201908)Basic and Advanced Research Projects of CSTC(No.cstc2019jcyj-zdxmX0008)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-K201900605).
文摘The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of network structure,diversity of network nodes,and sparsity of data all pose difficulties in predicting propagation.This paper proposes a malware propagation prediction model based on representation learning and Graph Convolutional Networks(GCN)to address the aforementioned problems.First,to solve the problem of the inaccuracy of infection intensity calculation caused by the sparsity of node interaction behavior data in the malware propagation network,a mechanism based on a tensor to mine the infection intensity among nodes is proposed to retain the network structure information.The influence of the relationship between nodes on the infection intensity is also analyzed.Second,given the diversity and complexity of the content and structure of infected and normal nodes in the network,considering the advantages of representation learning in data feature extraction,the corresponding representation learning method is adopted for the characteristics of infection intensity among nodes.This can efficiently calculate the relationship between entities and relationships in low dimensional space to achieve the goal of low dimensional,dense,and real-valued representation learning for the characteristics of propagation spatial data.We also design a new method,Tensor2vec,to learn the potential structural features of malware propagation.Finally,considering the convolution ability of GCN for non-Euclidean data,we propose a dynamic prediction model of malware propagation based on representation learning and GCN to solve the time effectiveness problem of the malware propagation carrier.The experimental results show that the proposed model can effectively predict the behaviors of the nodes in the network and discover the influence of different characteristics of nodes on the malware propagation situation.
基金The National Natural Science Foundation of China(No.61762031)The Science and Technology Major Project of Guangxi Province(NO.AA19046004)The Natural Science Foundation of Guangxi(No.2021JJA170130).
文摘There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem.Meanwhile, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.
基金supported by the Natural Science Foundation of Zhejiang Province(LY19A020001).
文摘With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.
基金National Natural Science Foundation of China(No.61972080)Shanghai Rising-Star Program,China(No.19QA1400300)。
文摘With the wide application of location-based social networks(LBSNs),personalized point of interest(POI)recommendation becomes popular,especially in the commercial field.Unfortunately,it is challenging to accurately recommend POIs to users because the user-POI matrix is extremely sparse.In addition,a user's check-in activities are affected by many influential factors.However,most of existing studies capture only few influential factors.It is hard for them to be extended to incorporate other heterogeneous information in a unified way.To address these problems,we propose a meta-path-based deep representation learning(MPDRL)model for personalized POI recommendation.In this model,we design eight types of meta-paths to fully utilize the rich heterogeneous information in LBSNs for the representations of users and POIs,and deeply mine the correlations between users and POIs.To further improve the recommendation performance,we design an attention-based long short-term memory(LSTM)network to learn the importance of different influential factors on a user's specific check-in activity.To verify the effectiveness of our proposed method,we conduct extensive experiments on a real-world dataset,Foursquare.Experimental results show that the MPDRL model improves at least 16.97%and 23.55%over all comparison methods in terms of the metric Precision@N(Pre@N)and Recall@N(Rec@N)respectively.
基金This work was supported by the Research Deanship of Prince Sattam Bin Abdulaziz University,Al-Kharj,Saudi Arabia(Grant No.2020/01/17215).Also,the author thanks Deanship of college of computer engineering and sciences for technical support provided to complete the project successfully。
文摘In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart performance.Yet,these models fail to consider the classification error while learning the feature representation.Intuitively,the learnt feature representation may degrade the performance of the classification task.For the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion detection.Specially,the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously.This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection.Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model.First,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed model.Next,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed model.Finally,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.
基金supported by the Hong Kong-Macao-Taiwan Science and Technology Cooperation Project of the Science and Technology Innovation Action Plan in Shanghai(Grant No.23510760200)the Oriental Talent Youth Program of Shanghai(Grant No.Y3DFRCZL01)+4 种基金the Outstanding Program of the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y2023080)the Strategic Priority Research Program of the Chinese Academy of Sciences(Category A)(Grant No.XDA0360404)the MYHT Program of China(Grant No.D030312)the Shanghai Pujiang Program(Grant No.23pjd092)the Shanghai Pilot Program for Basic Research–Chinese Academy of Science,Shanghai Branch(Grant No.JCYJ-SHFY-2022-015)。
文摘Satellite communication provides crucial connectivity for low-altitude aircraft to address coverage gaps in groundbased internet and aerial communication blind spots.Achieving this capability requires robust anomaly detection methods.However,existing approaches struggle to capture the complex spatio-temporal relationships in space-air communications due to challenges such as unpredictable network topology from real-time route adjustments,the absence of predefined communication link patterns,and rapid spatio-temporal relationship evolution among moving nodes.To address these challenges,this paper proposes a graph representation anomaly detection framework tailored for communication networks between low Earth orbit(LEO)satellite constellations and low-altitude aircraft.The spatio-temporal relationships between satellites and aircraft are modeled using dynamic graph structures,which capture 3D locations of each node and anomaly characteristics of each link.The proposed framework is compatible with diverse anomaly detection learning algorithms,and several static and dynamic detection algorithms are evaluated and compared in this work.Furthermore,authors present an improved variant of a novel transformer-based anomaly detection framework for dynamic graphs(TADDY),which involves temporal periodic encoding,semi-supervised comparative learning,and multiscale graph attention mechanisms.We evaluate the framework through simulated scenarios,comparing Walker and broken-chain constellations with varying air traffic densities in a specific region.The anomaly detection performances at different anomaly ratios and air traffic densities are evaluated.Experimental results demonstrate that,the performances of static anomaly detection methods and TADDY algorithm degrade significantly as air traffic density increases.Meanwhile,the proposed improved TADDY achieves an average AUC of 0.95 for Walker constellation and 0.86 for the broken-chain constellation,outperforming the original TADDY in both accuracy and reliability under high anomaly rates.Finally,sensitivity analysis and ablation studies confirm the framework's high responsiveness to anomalies such as abrupt topological changes,offering an efficient solution for ensuring the reliability of large-scale satellite-aircraft communication systems.
基金supported by research grants from China Petroleum&Chemical Corp(funding number 124014)the financial support from the National Key R&D Program of China(Grant No.2024YFA1510200).
文摘The escalating energy crisis has spurred extensive research into organic compounds for energyefficient applications,taking advantage of their environmental friendliness,cost-effective synthesis,and adaptable molecular structures.Traditional trial-and-error methods for discovering highly functional organic compounds are expensive and time-consuming.We employed a 3D transformerbased molecular representation learning algorithm to create the Org-Mol pre-trained model,using 60 million semi-empirically optimized small organic molecule structures.After fine-tuning with public experimental data,the model can accurately predict various physical properties of pure organics,with test set R2 values exceeding 0.92.These fine-tuned models are used in high-throughput screening among millions of ester molecules to identify novel immersion coolants,resulting in the experimental validation of two promising candidates.This work not only demonstrates the potential of Org-Mol in predicting bulk properties for pure organic compounds but also paves the way for the rational and efficient development of ideal candidates for energy-saving materials.
文摘The effectiveness of machine learning(ML)models for architectural applications relies on high-quality datasets balanced with advancements in model architecture and computational capacity.Current methods for evaluating architectural floor plan datasets typically depend on explicit semantic annotations,which limit their effectiveness and scalability when annotations are unavailable or inconsistent.To address this limitation,this research develops an isovist-based latent representation approach to quantitatively measure typicality and diversity within architectural datasets without relying on semantic labels.We introduce Isovist Latent Norm Typicality,a metric that leverages the statistical structure of latent representations derived from isovist morphological features using a variational autoencoder(VAE).This metric quantifies typicality by analyzing distributional shifts in latent representations between individual floor plans and a reference dataset using a modified Wasserstein distance.Experimental results demonstrate the approach’s ability to distinguish typical from atypical floor plan configurations,capturing the morphological features that complement conventional metrics.Comparative analysis indicates that our method provides insights into spatial organization,correlating with conventional properties such as programmatic diversity and spatial openness.By quantifying typicality through purely morphological features,the proposed methodology facilitates dataset curation prior to costly semantic annotation,enhancing dataset quality and enabling scalability to more extensive and diverse architectural datasets.
基金supported in part by NSFC’s Major Research Project 92270001Z.Z.’s work is supported in part by the Beijing Nova Program(20250484934).
文摘The rational design of organic functional devices relies on understanding structure-propertyperformance relationships through multi-scale characterization.However,traditional characterizations are costly and require multidisciplinary expertise.Here we present OCNet,a domain-knowledge-enhanced representation learning framework that,for the first time,enables unified virtual characterization from molecules to devices.Pre-trained on over ten million selfgenerated conjugated molecules and dimers,OCNet learns generalizable microscopic representations comparable to expert-crafted features.As a result,it surpasses state-of-the-art models by over 20%in predicting key computed and experimental molecular optoelectronic properties.OCNet further provides the first transferable model for predicting transfer integrals in thin films,enabling accurate mesoscale carrier mobility estimation via multiscale simulations.By integrating tight-binding-level electronic descriptors,OCNet achieves near real-time,accurate prediction of device power conversion efficiency.Together,OCNet offers a unified and scalable foundation for virtual characterization of organic materials across multiple scales,with broad applicability in photovoltaics,displays,and sensing.
基金supported by the National Natural Science Foundation of China(Grant No.61772249).
文摘With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Current trajectory data processing methods typically require input in the form of fixed-length vectors,making it crucial to convert variable-length trajectory data into fixed-length,low-dimensional embedding vectors.Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations.This paper provides a comprehensive review of the research progress,methodologies,and applications of trajectory representation learning.First,it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets.Then,it classifies trajectory representation learning methods based on various downstream tasks,with a focus on their principles,advantages,limitations,and application scenarios in trajectory similarity computation,similar trajectory search,trajectory clustering,and trajectory prediction.Additionally,representative model structures and principles in each task are analyzed,along with the characteristics and advantages of different methods in each task.Last,the challenges faced by current trajectory representation learning methods are analyzed,including data sparsity,multimodality,model optimization,and privacy protection,while potential research directions and methodologies to address these challenges are explored.
基金supported by the“pioneer”and“Leading Goose”Key R&D Program of Zhejiang Province under Grant No.2022C03106the Zhejiang Provincial Natural Science Foundation of China under Grant No.LY23F020010the National Natural Science Foundation of China under Grant No.62077015.
文摘Entity alignment,which aims to identify entities with the same meaning in different Knowledge Graphs(KGs),is a key step in knowledge integration.Despite the promising results achieved by existing methods,they often fail to fully leverage the structure information of KGs for entity alignment.Therefore,our goal is to thoroughly explore the features of entity neighbors and relationships to obtain better entity embeddings.In this work,we propose DCEA,an effective dual-context representation learning framework for entity alignment.Specifically,the neighbor-level embedding module introduces relation information to more accurately aggregate neighbor context.The relation-level embedding module utilizes neighbor context to enhance relation-level embeddings.To eliminate semantic gaps between neighbor-level and relation-level embeddings,and fully exploit their complementarity,we design a hybrid embedding fusion model that adaptively performs embedding fusion to obtain powerful joint entity embeddings.We also jointly optimize the contrastive loss of multi-level embeddings,enhancing their mutual reinforcement while preserving the characteristics of neighbor and relation embeddings.Additionally,the decision fusion module combines the similarity scores calculated between entities based on embeddings at different levels to make the final alignment decision.Extensive experimental results on public datasets indicate that our DCEA performs better than state-of-the-art baselines.
基金supported by the National Natural Science Foundation of China(No.U24A20256)and the Science and Technology Major Project of Changsha(No.kh2402004).
文摘Predicting mortality risk in the Intensive Care Unit(ICU)using Electronic Medical Records(EMR)is crucial for identifying patients in need of immediate attention.However,the incompleteness and the variability of EMR features for each patient make mortality prediction challenging.This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges.The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data,enabling the effective and explainable fusion of the incomplete features.Modality-specific encoders are employed to encode each modality feature separately.To tackle the variability and incompleteness of input features among patients,a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs.Furthermore,a MultiModal Gated Contrastive Representation Learning(MMGCRL)method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance.We evaluate the proposed framework using the large-scale ICU dataset,MIMIC-III.Experimental results demonstrate its effectiveness in mortality prediction,outperforming several state-of-the-art methods.
