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 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
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
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
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
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
Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or ...Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall precision.To address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh datasets.Methods FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.Results Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline MeshNet.Furthermore,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.展开更多
Deep forgery detection technologies are crucial for image and video recognition tasks,with their performance heavily reliant on the features extracted from both real and fake images.However,most existing methods prima...Deep forgery detection technologies are crucial for image and video recognition tasks,with their performance heavily reliant on the features extracted from both real and fake images.However,most existing methods primarily focus on spatial domain features,which limits their accuracy.To address this limitation,we propose an adaptive dual-domain feature representation method for enhanced deep forgery detection.Specifically,an adaptive region dynamic convolution module is established to efficiently extract facial features from the spatial domain.Then,we introduce an adaptive frequency dynamic filter to capture effective frequency domain features.By fusing both spatial and frequency domain features,our approach significantly improves the accuracy of classifying real and fake facial images.Finally,experimental results on three real-world datasets validate the effectiveness of our dual-domain feature representation method,which substantially improves classification precision.展开更多
Sparse representation has been highly successful in various tasks related to image processing and computer vision.For ancient mural image inpainting,traditional group sparse representation models usually lead to struc...Sparse representation has been highly successful in various tasks related to image processing and computer vision.For ancient mural image inpainting,traditional group sparse representation models usually lead to structure blur and line discontinuity due to the construction of similarity group solely based on the Euclidean distance and the randomness of dictionary initialization.To address the aforementioned issues,an improved curvature Gabor transform and group sparse representation(CGabor-GSR)model for ancient Dunhuang mural inpainting is proposed.To begin with,mutual information is introduced to weight the Euclidean distance,and then the weighted Euclidean distance acts as a new standard of similarity group.Subsequently,to mitigate the randomness of dictionary initialization,a curvature Gabor wavelet transform is proposed to extract the features and initialize the feature dictionary with dimension reduction based on principal component analysis(PCA).Ultimately,singular value decomposition(SVD)and split Bregman iteration(SBI)can be used to resolve the CGabor-GSR model to reconstruct the mural images.Experimental results on Dunhuang mural inpainting demonstrate tha the proposed CGabor-GSR achieves a better performance than compared algorithms in both objective and visual evaluation.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
Label-free 3D tomography has attracted growing attention in biological imaging due to its inherent resistance to phototoxicity and concise system configuration.Among existing techniques,Fourier ptychographic tomograph...Label-free 3D tomography has attracted growing attention in biological imaging due to its inherent resistance to phototoxicity and concise system configuration.Among existing techniques,Fourier ptychographic tomography(FPT)stands out for high-resolution refractive index(RI)reconstruction from noninterferometric measurements,avoiding coherent noise and phase instability-key limitations of optical diffraction tomography.However,conventional FPT suffers from significant artifacts and high computational demands,especially for multiscattering samples and long-term observation.Here,we introduce physicsinformed aberration-corrected meta neural representation(PAMR),an advanced self-supervised framework that integrates neural representation with physics prior,meta-learning optimization,and adaptive aberration correction.Simulations and experiments show that PAMR produces high-fidelity 3D reconstructions with reduced artifacts and strong optical section ability,achieving 137 and 550 nm resolution for lateral and axial,respectively.Moreover,PAMR exhibits superior sparse-view robustness,sustaining high-quality with 75%view reduction.Through the meta-learning strategy,the reconstruction speed of dynamic volumes could be increased by 10 times.Applications include 3D RI imaging of multiscattering C.elegans and long-term 3D observation of HeLa cells,showing detailed organelle structures and interactions.As a generalizable approach combining computational efficiency with physical accuracy,PAMR provides an advanced algorithm for label-free 3D microscopy,with broad applicability across biomedical research.展开更多
Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing method...Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing methods often fail to distinguish salient objects from background regions,leading to detail suppression in salient regions due to global fusion strategies.This study presents a mask-guided latent low-rank representation fusion method to address this issue.First,the GrabCut algorithm is employed to extract a saliency mask,distinguishing salient regions from background regions.Then,latent low-rank representation(LatLRR)is applied to extract deep image features,enhancing key information extraction.In the fusion stage,a weighted fusion strategy strengthens infrared thermal information and visible texture details in salient regions,while an average fusion strategy improves background smoothness and stability.Experimental results on the TNO dataset demonstrate that the proposed method achieves superior performance in SPI,MI,Qabf,PSNR,and EN metrics,effectively preserving salient target details while maintaining balanced background information.Compared to state-of-the-art fusion methods,our approach achieves more stable and visually consistent fusion results.The fusion code is available on GitHub at:https://github.com/joyzhen1/Image(accessed on 15 January 2025).展开更多
概念漂移是数据流挖掘中不可避免的难点问题,其典型特征是数据分布随时间可能发生改变.针对现有模型处理数据流分类任务时出现过拟合的问题,本文提出了一种目标解耦驱动的在线深度网络(Online Deep Network driven by Target Decoupling...概念漂移是数据流挖掘中不可避免的难点问题,其典型特征是数据分布随时间可能发生改变.针对现有模型处理数据流分类任务时出现过拟合的问题,本文提出了一种目标解耦驱动的在线深度网络(Online Deep Network driven by Target Decoupling,ODNTD).首先,该模型从历史数据流中学习一个任务未知型特征提取器,实现了对任务的无偏见表示学习,从而增强了模型的泛化能力;其次,模型利用任务特定的权重调整,使得任务未知的通用特征表示能够适应具体任务,通过这种目标任务的权重学习进一步提升了模型的适应性.实验结果表明,所提出的方法对含概念漂移的数据流有良好的泛化性能.展开更多
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(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.
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.
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.
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 National Key R&D Program of China(2022YFC3803600).
文摘Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall precision.To address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh datasets.Methods FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.Results Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline MeshNet.Furthermore,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.
基金supported in part by the National Natural Science Foundation of China under No.12401679the Nature Science Foundation of the Jiangsu Higher Education Institutions of China under No.23KJB520006the Haizhou Bay Talent Innovation Program of Jiangsu Ocean University under No.PD2024026。
文摘Deep forgery detection technologies are crucial for image and video recognition tasks,with their performance heavily reliant on the features extracted from both real and fake images.However,most existing methods primarily focus on spatial domain features,which limits their accuracy.To address this limitation,we propose an adaptive dual-domain feature representation method for enhanced deep forgery detection.Specifically,an adaptive region dynamic convolution module is established to efficiently extract facial features from the spatial domain.Then,we introduce an adaptive frequency dynamic filter to capture effective frequency domain features.By fusing both spatial and frequency domain features,our approach significantly improves the accuracy of classifying real and fake facial images.Finally,experimental results on three real-world datasets validate the effectiveness of our dual-domain feature representation method,which substantially improves classification precision.
基金supported by National Natural Science Foundation of China(No.61963023)Humanities and Social Sciences Youth Foundation of Ministry of Education(No.19YJC760012)Lanzhou Jiaotong University Basic Top-Notch Personnel Project(No.2022JC36).
文摘Sparse representation has been highly successful in various tasks related to image processing and computer vision.For ancient mural image inpainting,traditional group sparse representation models usually lead to structure blur and line discontinuity due to the construction of similarity group solely based on the Euclidean distance and the randomness of dictionary initialization.To address the aforementioned issues,an improved curvature Gabor transform and group sparse representation(CGabor-GSR)model for ancient Dunhuang mural inpainting is proposed.To begin with,mutual information is introduced to weight the Euclidean distance,and then the weighted Euclidean distance acts as a new standard of similarity group.Subsequently,to mitigate the randomness of dictionary initialization,a curvature Gabor wavelet transform is proposed to extract the features and initialize the feature dictionary with dimension reduction based on principal component analysis(PCA).Ultimately,singular value decomposition(SVD)and split Bregman iteration(SBI)can be used to resolve the CGabor-GSR model to reconstruct the mural images.Experimental results on Dunhuang mural inpainting demonstrate tha the proposed CGabor-GSR achieves a better performance than compared algorithms in both objective and visual evaluation.
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supported by the National Key Research and Development Program of China(Grant No.2022YFC3401100)the National Natural Science Foundation of China(Grant Nos.22534002,T2225014,62405099,32401253,and 62375095)+2 种基金the China Postdoctoral Science Foundation(Grant Nos.2024M750994 and 2024M761020)the Postdoctoral Project of Hubei Province(Grant Nos.2024HBBHCXA015 and 2024HBBHCXA013)the Key Research and Development Project of Hubei Province(Grant Nos.2024BCB011 and 2024BCB112).
文摘Label-free 3D tomography has attracted growing attention in biological imaging due to its inherent resistance to phototoxicity and concise system configuration.Among existing techniques,Fourier ptychographic tomography(FPT)stands out for high-resolution refractive index(RI)reconstruction from noninterferometric measurements,avoiding coherent noise and phase instability-key limitations of optical diffraction tomography.However,conventional FPT suffers from significant artifacts and high computational demands,especially for multiscattering samples and long-term observation.Here,we introduce physicsinformed aberration-corrected meta neural representation(PAMR),an advanced self-supervised framework that integrates neural representation with physics prior,meta-learning optimization,and adaptive aberration correction.Simulations and experiments show that PAMR produces high-fidelity 3D reconstructions with reduced artifacts and strong optical section ability,achieving 137 and 550 nm resolution for lateral and axial,respectively.Moreover,PAMR exhibits superior sparse-view robustness,sustaining high-quality with 75%view reduction.Through the meta-learning strategy,the reconstruction speed of dynamic volumes could be increased by 10 times.Applications include 3D RI imaging of multiscattering C.elegans and long-term 3D observation of HeLa cells,showing detailed organelle structures and interactions.As a generalizable approach combining computational efficiency with physical accuracy,PAMR provides an advanced algorithm for label-free 3D microscopy,with broad applicability across biomedical research.
基金supported by Universiti Teknologi MARA through UiTM MyRA Research Grant,600-RMC 5/3/GPM(053/2022).
文摘Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing methods often fail to distinguish salient objects from background regions,leading to detail suppression in salient regions due to global fusion strategies.This study presents a mask-guided latent low-rank representation fusion method to address this issue.First,the GrabCut algorithm is employed to extract a saliency mask,distinguishing salient regions from background regions.Then,latent low-rank representation(LatLRR)is applied to extract deep image features,enhancing key information extraction.In the fusion stage,a weighted fusion strategy strengthens infrared thermal information and visible texture details in salient regions,while an average fusion strategy improves background smoothness and stability.Experimental results on the TNO dataset demonstrate that the proposed method achieves superior performance in SPI,MI,Qabf,PSNR,and EN metrics,effectively preserving salient target details while maintaining balanced background information.Compared to state-of-the-art fusion methods,our approach achieves more stable and visually consistent fusion results.The fusion code is available on GitHub at:https://github.com/joyzhen1/Image(accessed on 15 January 2025).
文摘概念漂移是数据流挖掘中不可避免的难点问题,其典型特征是数据分布随时间可能发生改变.针对现有模型处理数据流分类任务时出现过拟合的问题,本文提出了一种目标解耦驱动的在线深度网络(Online Deep Network driven by Target Decoupling,ODNTD).首先,该模型从历史数据流中学习一个任务未知型特征提取器,实现了对任务的无偏见表示学习,从而增强了模型的泛化能力;其次,模型利用任务特定的权重调整,使得任务未知的通用特征表示能够适应具体任务,通过这种目标任务的权重学习进一步提升了模型的适应性.实验结果表明,所提出的方法对含概念漂移的数据流有良好的泛化性能.
