Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a m...A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a moving thin ground glass plate,is employed in a double-slit interference experiment.The ground glass plate induces random phase differences between light beams of different wavelengths passing through it.This initial random phase difference significantly influences the high-order intensity correlation functions of multi-wavelength thermal beams.Experimentally,second-order correlated interference patterns,including subwavelength interference,of pseudothermal beams with different wavelengths are observed in the intensity correlation measurements.This method facilitates applications of correlated thermal photons in quantum information processing and quantum imaging.展开更多
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp...Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.展开更多
One of the most significant challenges in commercializing organic second-order nonlinear optical(NLO)materials lies in the inherent trade-off between nonlinearity and stability.A key factor in mitigating this compromi...One of the most significant challenges in commercializing organic second-order nonlinear optical(NLO)materials lies in the inherent trade-off between nonlinearity and stability.A key factor in mitigating this compromise is achieving precise temporal synchronization between the formation of the cross-linked network and the establishment of an optimal non-centrosymmetric alignment of the chromophores.Guided by this principle,we developed a series of NLO polymers incorporating multiple ether chains with low rotational energy barriers,which facilitate molecular reorientation during electric field poling,thereby enhancing the NLO response effectively.Combined with an optimized photocrosslinking strategy,the resulting PX4o/PETMP doped film achieved large macroscopic NLO coefficient of 190 pm·V^(-1)and thermal degradation temperature as high as 120℃.This work offers a universal approach to alleviating the“nonlinearity-stability”trade-off in a wide range of polymeric systems.展开更多
Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high c...Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.展开更多
Based on the Cayley-Hamilton theorem and fixed-point method,we provide an elementary proof for the representation theorem of analytic isotropic tensor functions of a second-order tensor in a three-dimensional(3D)inner...Based on the Cayley-Hamilton theorem and fixed-point method,we provide an elementary proof for the representation theorem of analytic isotropic tensor functions of a second-order tensor in a three-dimensional(3D)inner-product space,which avoids introducing the generating function and Taylor series expansion.The proof is also extended to any finite-dimensional inner-product space.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-represent...Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-representation is either a string representation or a band representation by using the coefficient quivers.It is worth noting that for a given band and a positive integer,there exists a unique band representation up to isomorphism.展开更多
Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical paramete...Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical parameter descriptors,elemental-property descriptors,and descriptors extracted from the periodic table representation(PTR)by the convolutional neural network were collected.Appropriate selection among features with rich information is helpful for phase classification.Based on random forest,the accuracy of the four-label classification and balanced accuracy of the five-label classification were improved to be 0.907 and 0.876,respectively.The roles of the four important features were summarized by interpretability analysis,and a new important feature was found.The model extrapolation ability and the influence of Mo were demonstrated by phase prediction in(CoFeNiMn)_(1-x)Mo_(x).The phase information is helpful for the hardness prediction,the classification results were coupled with the PTR of hardness data,and the prediction error(the root mean square error)was reduced to 56.69.展开更多
Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays ...Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.展开更多
In recent years,the study of higher-order topological states and their material realizations has become a research frontier in topological condensed matter physics.We demonstrate that twisted bilayer graphene with sma...In recent years,the study of higher-order topological states and their material realizations has become a research frontier in topological condensed matter physics.We demonstrate that twisted bilayer graphene with small twist angles behaves as a second-order topological insulator possessing topological corner charges.Using a tight-binding model,we compute the topological band indices and corner states of finite-sized twisted bilayer graphene flakes.It is found that for any small twist angle,whether commensurate or incommensurate,the gaps both below and above the flat bands are associated with nontrivial topological indices.Our results not only extend the concept of second-order band topology to arbitrary small twist angles but also confirm the existence of corner states at acute-angle corners.展开更多
Continuous control protocols are extensively utilized in traditional MASs,in which information needs to be transmitted among agents consecutively,therefore resulting in excessive consumption of limited resources.To de...Continuous control protocols are extensively utilized in traditional MASs,in which information needs to be transmitted among agents consecutively,therefore resulting in excessive consumption of limited resources.To decrease the control cost,based on ISC,several LFC problems are investigated for second-order MASs without and with time delay,respectively.Firstly,an intermittent sampled controller is designed,and a sufficient and necessary condition is derived,under which state errors between the leader and all the followers approach zero asymptotically.Considering that time delay is inevitable,a new protocol is proposed to deal with the time-delay situation.The error system’s stability is analyzed using the Schur stability theorem,and sufficient and necessary conditions for LFC are obtained,which are closely associated with the coupling gain,the system parameters,and the network structure.Furthermore,for the case where the current position and velocity information are not available,a distributed protocol is designed that depends only on the sampled position information.The sufficient and necessary conditions for LFC are also given.The results show that second-order MASs can achieve the LFC if and only if the system parameters satisfy the inequalities proposed in the paper.Finally,the correctness of the obtained results is verified by numerical simulations.展开更多
The purpose of this article is to depart from the conventional belief that John Donne,a vibrant 17th-century writer,is a full-blown metaphysical poet as widely claimed while also acknowledging the poetic ingenuity of ...The purpose of this article is to depart from the conventional belief that John Donne,a vibrant 17th-century writer,is a full-blown metaphysical poet as widely claimed while also acknowledging the poetic ingenuity of John Donne.While Donne’s poetry is rich in matter and manner,and his poems are caked in wit,intellectual superiority,and apt exploration of telling themes,dressing him fully in borrowed robes seems a stretch.Some of Donne’s poems,without a shred of doubt,contain flavors of metaphysical poetry,but the term“metaphysical”seems to be unsuitable for poems such as“A Valediction:Forbidding Mourning”.展开更多
Establishing and maintaining protected areas is a pivotal strategy for attaining the post-2020 biodiversity target. The conservation objectives of protected areas have shifted from a narrow emphasis on biodiversity to...Establishing and maintaining protected areas is a pivotal strategy for attaining the post-2020 biodiversity target. The conservation objectives of protected areas have shifted from a narrow emphasis on biodiversity to encompass broader considerations such as ecosystem stability, community resilience to climate change, and enhancement of human well-being. Given these multifaceted objectives, it is imperative to judiciously allocate resources to effectively conserve biodiversity by identifying strategically significant areas for conservation, particularly for mountainous areas. In this study, we evaluated the representativeness of the protected area network in the Qin ling Mountains concerning species diversity, ecosystem services, climate stability and ecological stability. The results indicate that some of the ecological indicators are spatially correlated with topographic gradient effects. The conservation priority areas predominantly lie in the northern foothills, the southeastern, and southwestern parts of the Qinling Mountain with areas concentrated at altitudes between 1,500-2,000 m and slopes between 40°-50° as hotspots. The conservation priority areas identified through the framework of inclusive conservation optimization account for 22.9 % of the Qinling Mountain. Existing protected areas comprise only 6.1 % of the Qinling Mountain and 13.18 % of the conservation priority areas. This will play an important role in achiev ing sustainable development in the region and in meeting the post-2020 biodiversity target. The framework can advance the different objectives of achieving a quadruple win and can also be extended to other regions.展开更多
In this paper,we investigate the phenomena of electromagnetically induced transparency and the generation of second-order sideband in a Laguerre–Gaussian cavity optorotational system with a Kerr nonlinear medium.Usin...In this paper,we investigate the phenomena of electromagnetically induced transparency and the generation of second-order sideband in a Laguerre–Gaussian cavity optorotational system with a Kerr nonlinear medium.Using the perturbation method,we analyze the first-and second-order sideband generations in the output field from the system under the actions of a strong control field and a weak probe field.Numerical simulations show that the Kerr nonlinearity can lead to the occurrence of the asymmetric line shape in the transmission of the probe field.Comparing with traditional scheme for generating the second-order sideband,our spectral shape of the second-order sideband is amplified and becomes asymmetric,which has potential applications in precision measurement,high-sensitivity devices,and frequency conversion.展开更多
Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extrac...Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.展开更多
The stabilization problem of second-order bilinear systems with time delay is investigated.Feedback controls are chosen so that the strong and exponential stabilization of the system is ensured.The obtained results ar...The stabilization problem of second-order bilinear systems with time delay is investigated.Feedback controls are chosen so that the strong and exponential stabilization of the system is ensured.The obtained results are illustrated by wave and beam equations with simulation.展开更多
In recent years,the transformer model has demonstrated excellent performance in computer vision(CV)applications.The key lies in its guided representation attention mechanism,which uses dot-product to depict complex fe...In recent years,the transformer model has demonstrated excellent performance in computer vision(CV)applications.The key lies in its guided representation attention mechanism,which uses dot-product to depict complex feature relationships,and comprehensively understands the context semantics to obtain feature weights.Then feature enhancement is implemented by guiding the target matrix through feature weights.However,the uncertainty and inconsistency of features are widespread that prone to confusion in the description of relationships within dot-product attention mechanisms.To solve this problem,this paper proposed a novel approximate-guided representation learning methodology for vision transformer.The kernelised matroids fuzzy rough set is defined,wherein the closed sets inside kernelised fuzzy information granules of matroids structures can constitute the subspace of lower approximation in rough sets.Thus,the kernel relation is employed to characterise image feature granules that will be reconstructed according to the independent set in matroids theory.Then,according to the characteristics of the closed set within matroids,the feature attention weight is formed by using the lower approximation to realise the approximate guidance of features.The approximate-guided representation mechanism can be flexibly deployed as a plug-and-play component in a wide range of CV tasks.Extensive empirical results demonstrate that the proposed method outperforms the majority of advanced prevalent models,especially in terms of robustness.展开更多
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
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
文摘Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
基金supported by the National Natural Science Foundation of China(Grant Nos.62105278 and 11674273)the Natural Science Foundation of Shandong Province(Grant No.ZR2023MA015)。
文摘A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a moving thin ground glass plate,is employed in a double-slit interference experiment.The ground glass plate induces random phase differences between light beams of different wavelengths passing through it.This initial random phase difference significantly influences the high-order intensity correlation functions of multi-wavelength thermal beams.Experimentally,second-order correlated interference patterns,including subwavelength interference,of pseudothermal beams with different wavelengths are observed in the intensity correlation measurements.This method facilitates applications of correlated thermal photons in quantum information processing and quantum imaging.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128 and 62306139the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.
基金supported by the National Natural Science Foundation of China(Nos.22235006 and 22475157)Foundation of Hubei Scientific Committee(Nos.2024 AFA021 and 2024BAB014)Fundamental Research Funds for the Central Universities(No.2042025kf0009)。
文摘One of the most significant challenges in commercializing organic second-order nonlinear optical(NLO)materials lies in the inherent trade-off between nonlinearity and stability.A key factor in mitigating this compromise is achieving precise temporal synchronization between the formation of the cross-linked network and the establishment of an optimal non-centrosymmetric alignment of the chromophores.Guided by this principle,we developed a series of NLO polymers incorporating multiple ether chains with low rotational energy barriers,which facilitate molecular reorientation during electric field poling,thereby enhancing the NLO response effectively.Combined with an optimized photocrosslinking strategy,the resulting PX4o/PETMP doped film achieved large macroscopic NLO coefficient of 190 pm·V^(-1)and thermal degradation temperature as high as 120℃.This work offers a universal approach to alleviating the“nonlinearity-stability”trade-off in a wide range of polymeric systems.
基金supported by the National Natural Science Foundation of China(62225303,62403043,62433004)the Beijing Natural Science Foundation(4244085)+1 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20230203)the China Postdoctoral Science Foundation(2023M740201)。
文摘Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.
文摘Based on the Cayley-Hamilton theorem and fixed-point method,we provide an elementary proof for the representation theorem of analytic isotropic tensor functions of a second-order tensor in a three-dimensional(3D)inner-product space,which avoids introducing the generating function and Taylor series expansion.The proof is also extended to any finite-dimensional inner-product space.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
文摘Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-representation is either a string representation or a band representation by using the coefficient quivers.It is worth noting that for a given band and a positive integer,there exists a unique band representation up to isomorphism.
基金supported by the National Natural Science Foundation of China(Nos.51671075,51971086)the Natural Science Foundation of Heilongjiang Province,China(No.LH2022E081)。
文摘Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical parameter descriptors,elemental-property descriptors,and descriptors extracted from the periodic table representation(PTR)by the convolutional neural network were collected.Appropriate selection among features with rich information is helpful for phase classification.Based on random forest,the accuracy of the four-label classification and balanced accuracy of the five-label classification were improved to be 0.907 and 0.876,respectively.The roles of the four important features were summarized by interpretability analysis,and a new important feature was found.The model extrapolation ability and the influence of Mo were demonstrated by phase prediction in(CoFeNiMn)_(1-x)Mo_(x).The phase information is helpful for the hardness prediction,the classification results were coupled with the PTR of hardness data,and the prediction error(the root mean square error)was reduced to 56.69.
基金supported by the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)the Shenzhen Science and Technology Program(No.JCYJ20230807140709020)+2 种基金National Natural Science Foundation of China(Nos.62402489,U22A2041,and 62373172)the China Postdoctoral Science Foundation(No.2023M743688)Guangdong Basic and Applied Basic Research Foundation(Nos.2024A1515011960 and 2023A1515110570)。
文摘Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104232 and 12074156).
文摘In recent years,the study of higher-order topological states and their material realizations has become a research frontier in topological condensed matter physics.We demonstrate that twisted bilayer graphene with small twist angles behaves as a second-order topological insulator possessing topological corner charges.Using a tight-binding model,we compute the topological band indices and corner states of finite-sized twisted bilayer graphene flakes.It is found that for any small twist angle,whether commensurate or incommensurate,the gaps both below and above the flat bands are associated with nontrivial topological indices.Our results not only extend the concept of second-order band topology to arbitrary small twist angles but also confirm the existence of corner states at acute-angle corners.
基金supported by the National Natural Science Foundation of China under Grants 62476138 and 42375016.
文摘Continuous control protocols are extensively utilized in traditional MASs,in which information needs to be transmitted among agents consecutively,therefore resulting in excessive consumption of limited resources.To decrease the control cost,based on ISC,several LFC problems are investigated for second-order MASs without and with time delay,respectively.Firstly,an intermittent sampled controller is designed,and a sufficient and necessary condition is derived,under which state errors between the leader and all the followers approach zero asymptotically.Considering that time delay is inevitable,a new protocol is proposed to deal with the time-delay situation.The error system’s stability is analyzed using the Schur stability theorem,and sufficient and necessary conditions for LFC are obtained,which are closely associated with the coupling gain,the system parameters,and the network structure.Furthermore,for the case where the current position and velocity information are not available,a distributed protocol is designed that depends only on the sampled position information.The sufficient and necessary conditions for LFC are also given.The results show that second-order MASs can achieve the LFC if and only if the system parameters satisfy the inequalities proposed in the paper.Finally,the correctness of the obtained results is verified by numerical simulations.
文摘The purpose of this article is to depart from the conventional belief that John Donne,a vibrant 17th-century writer,is a full-blown metaphysical poet as widely claimed while also acknowledging the poetic ingenuity of John Donne.While Donne’s poetry is rich in matter and manner,and his poems are caked in wit,intellectual superiority,and apt exploration of telling themes,dressing him fully in borrowed robes seems a stretch.Some of Donne’s poems,without a shred of doubt,contain flavors of metaphysical poetry,but the term“metaphysical”seems to be unsuitable for poems such as“A Valediction:Forbidding Mourning”.
基金supported by the National Natural Science Foun-dation of China(Grant No.72349002).
文摘Establishing and maintaining protected areas is a pivotal strategy for attaining the post-2020 biodiversity target. The conservation objectives of protected areas have shifted from a narrow emphasis on biodiversity to encompass broader considerations such as ecosystem stability, community resilience to climate change, and enhancement of human well-being. Given these multifaceted objectives, it is imperative to judiciously allocate resources to effectively conserve biodiversity by identifying strategically significant areas for conservation, particularly for mountainous areas. In this study, we evaluated the representativeness of the protected area network in the Qin ling Mountains concerning species diversity, ecosystem services, climate stability and ecological stability. The results indicate that some of the ecological indicators are spatially correlated with topographic gradient effects. The conservation priority areas predominantly lie in the northern foothills, the southeastern, and southwestern parts of the Qinling Mountain with areas concentrated at altitudes between 1,500-2,000 m and slopes between 40°-50° as hotspots. The conservation priority areas identified through the framework of inclusive conservation optimization account for 22.9 % of the Qinling Mountain. Existing protected areas comprise only 6.1 % of the Qinling Mountain and 13.18 % of the conservation priority areas. This will play an important role in achiev ing sustainable development in the region and in meeting the post-2020 biodiversity target. The framework can advance the different objectives of achieving a quadruple win and can also be extended to other regions.
基金supported by the National Natural Science Foundation of China(Grant Nos.12174344 and 12175199)Foundation of Department of Science and Technology of Zhejiang Province(Grant No.2022R52047)。
文摘In this paper,we investigate the phenomena of electromagnetically induced transparency and the generation of second-order sideband in a Laguerre–Gaussian cavity optorotational system with a Kerr nonlinear medium.Using the perturbation method,we analyze the first-and second-order sideband generations in the output field from the system under the actions of a strong control field and a weak probe field.Numerical simulations show that the Kerr nonlinearity can lead to the occurrence of the asymmetric line shape in the transmission of the probe field.Comparing with traditional scheme for generating the second-order sideband,our spectral shape of the second-order sideband is amplified and becomes asymmetric,which has potential applications in precision measurement,high-sensitivity devices,and frequency conversion.
基金the financial support from Natural Science Foundation of Gansu Province(Nos.22JR5RA217,22JR5RA216)Lanzhou Science and Technology Program(No.2022-2-111)+1 种基金Lanzhou University of Arts and Sciences School Innovation Fund Project(No.XJ2022000103)Lanzhou College of Arts and Sciences 2023 Talent Cultivation Quality Improvement Project(No.2023-ZL-jxzz-03)。
文摘Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.
文摘The stabilization problem of second-order bilinear systems with time delay is investigated.Feedback controls are chosen so that the strong and exponential stabilization of the system is ensured.The obtained results are illustrated by wave and beam equations with simulation.
基金supported in part by the National Natural Science Foundation of China(62471205,62462040)Yunnan Fundamental Research Projects(202301AV070003)+1 种基金Major Science and Technology Projects in Yunnan Province(202302AG050009,202202AD080013)Sichuan Provincial Key Laboratory of Philosophy and Social Science for Language Intelligence in Special Education Major Project(YYZN-2024-1).
文摘In recent years,the transformer model has demonstrated excellent performance in computer vision(CV)applications.The key lies in its guided representation attention mechanism,which uses dot-product to depict complex feature relationships,and comprehensively understands the context semantics to obtain feature weights.Then feature enhancement is implemented by guiding the target matrix through feature weights.However,the uncertainty and inconsistency of features are widespread that prone to confusion in the description of relationships within dot-product attention mechanisms.To solve this problem,this paper proposed a novel approximate-guided representation learning methodology for vision transformer.The kernelised matroids fuzzy rough set is defined,wherein the closed sets inside kernelised fuzzy information granules of matroids structures can constitute the subspace of lower approximation in rough sets.Thus,the kernel relation is employed to characterise image feature granules that will be reconstructed according to the independent set in matroids theory.Then,according to the characteristics of the closed set within matroids,the feature attention weight is formed by using the lower approximation to realise the approximate guidance of features.The approximate-guided representation mechanism can be flexibly deployed as a plug-and-play component in a wide range of CV tasks.Extensive empirical results demonstrate that the proposed method outperforms the majority of advanced prevalent models,especially in terms of robustness.
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.
