In this paper, the authors propose a new approach to image compression based on the principle of Set Partitioning in Hierarchical Tree algorithm （SPIHT）. Our approach, the modified SPIHT （MSPIHT）, distributes entr...In this paper, the authors propose a new approach to image compression based on the principle of Set Partitioning in Hierarchical Tree algorithm （SPIHT）. Our approach, the modified SPIHT （MSPIHT）, distributes entropy differently than SPIHT and also optimizes the coding. This approach can produce results that are a significant improvement on the Peak Signal-to-Noise Ratio （PSNR） and compression ratio obtained by SPIHT algorithm, without affecting the computing time. These results are also comparable with those obtained using the Embedded Zerotree Wavelet （EZW） and Joint Photographic Experts Group 2000 （JPG2） algorithms.展开更多
Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as e...Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization capabilities.To address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance.The AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence features.Compared with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP recognition.The comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.展开更多
Based on the general [Mo] equivalent criterion and d-electron orbital theory, a new ultrahigh-strength βtitanium alloy with eight major elements(Ti-4.5Al-6.5Mo-2Cr-2.6Nb-2Zr-2Sn-1V, TB17) for industrial applications ...Based on the general [Mo] equivalent criterion and d-electron orbital theory, a new ultrahigh-strength βtitanium alloy with eight major elements(Ti-4.5Al-6.5Mo-2Cr-2.6Nb-2Zr-2Sn-1V, TB17) for industrial applications was developed. An ingot of five tons was successfully melted by thrice vacuum consumable arc melting. The microstructure and elements partitioning of different conditions were investigated systematically. The results suggest that the hierarchical structures of micro-scale first α phase(αf), nano-scale secondary α phase(αs), and ultrafine FCC substructures can be tailored by solution plus aging(STA) heat treatment. The lateral and epitaxial growth of αfphase promotes the HCP-α to FCC substructure transformation with the help of elements partitioning during the aging process. Moreover, the element V, generally regarded as β stabilizer, is found to mainly concentrate in the Al-rich αfphase in this study probably due to its relatively lower content and the strong bonding energy of Al-V. The hierarchical structure has a strong interaction with dislocations, which contributes to achieve a superhigh strength of 1376 MPa.In addition, the plastic strain is partitioned in the multi-scale precipitates(such as the α and FCC substructures) and β matrix, resulting in a considerable plasticity. TEM observation demonstrates that high density entangled dislocations at interfaces and mechanical twins exist in the STA sample after tensile test. It can be deduced that both dislocation slipping and twinning mechanisms are present in this alloy.Therefore, TB17 alloy can serve as an excellent candidate for structural materials on aircrafts that require high strength and lightweight.展开更多
Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acqu...Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images,which poses great challenges for subsequent applications.Therefore,an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time.In this work,we proposed a multi-scale hierarchical representation,i.e.binary partition tree,for analyzing large-scale UAV images.More precisely,we first obtained an initial partition of images by an oversegmentation algorithm,i.e.the simple linear iterative clustering.Next,we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships.Moreover,objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure.Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.展开更多
The truncated hierarchical B-spline basis has been proposed for adaptive data fitting and has already drawn a lot of attention in theory and applications. However the stability with respect to the Lp-norm, 1 ≤ p 〈 ...The truncated hierarchical B-spline basis has been proposed for adaptive data fitting and has already drawn a lot of attention in theory and applications. However the stability with respect to the Lp-norm, 1 ≤ p 〈 ∞, is not clear. In this paper, we consider the Lp stability of the truncated hierarchical B-spline basis, since the Lp stability is useful for curve and surface fitting, especially for least squares fitting. We prove that this basis is weakly Lp stable. This means that the associated constants to be considered in the stability analysis are at most of polynomial growth in the number of the hierarchy depth.展开更多
Hierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels simultaneously.Existing methods tend to overlook that different image region...Hierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels simultaneously.Existing methods tend to overlook that different image regions contribute differently to label prediction at different granularities,and also insufficiently consider relationships between the hierarchical multi-granularity labels.We introduce a sequence-to-sequence mechanism to overcome these two problems and propose a multi-granularity sequence generation(MGSG)approach for the hierarchical multi-granularity image classification task.Specifically,we introduce a transformer architecture to encode the image into visual representation sequences.Next,we traverse the taxonomic tree and organize the multi-granularity labels into sequences,and vectorize them and add positional information.The proposed multi-granularity sequence generation method builds a decoder that takes visual representation sequences and semantic label embedding as inputs,and outputs the predicted multi-granularity label sequence.The decoder models dependencies and correlations between multi-granularity labels through a masked multi-head self-attention mechanism,and relates visual information to the semantic label information through a crossmodality attention mechanism.In this way,the proposed method preserves the relationships between labels at different granularity levels and takes into account the influence of different image regions on labels with different granularities.Evaluations on six public benchmarks qualitatively and quantitatively demonstrate the advantages of the proposed method.Our project is available at https://github.com/liuxindazz/mgs.展开更多
The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized...The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns,hindering artificial intelligence from empowering mobile devices.Moreover,these data are not identically and independently distributed(Non-IID)caused by their different context,which will deteriorate the performance of the model.To address these issues,we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation,named ADC-DL,which learns a shared model by collecting the synthetic samples generated on each device.To tackle the heterogeneity of data distribution,we propose an entropy topsis comprehensive tiering model for hierarchical clustering,which distinguishes clients in terms of their data characteristics.Subsequently,synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation.The procedure of dataset condensation can be adjusted adaptively according to the tier of the client.Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.展开更多
分布式资源大规模并网要求配电网的灵活调控能力不断增强,如何充分利用多层级灵活性资源协助系统运行成为目前亟待解决的问题。为此,文中提供一种支撑多种资源接入配电网的分级自治协同策略。首先,分析多层级下灵活性资源特性,对分布式...分布式资源大规模并网要求配电网的灵活调控能力不断增强,如何充分利用多层级灵活性资源协助系统运行成为目前亟待解决的问题。为此,文中提供一种支撑多种资源接入配电网的分级自治协同策略。首先,分析多层级下灵活性资源特性,对分布式资源出力采用概率模型以减少其不确定性因素影响。其次,构建主变-馈线-台区分层分区优化调度模型,台区层进行内部自治并将等值结果传递给馈线层,馈线层基于网络架构和资源运行特性进行区域划分,实现兼顾系统安全性和经济性的主配协同优化,并采用基于谱惩罚参数的自适应交替方向乘子法(spectral penalty parameter based adaptive alternating direction method of multipliers,SPPA-ADMM)进行求解。最后,选用改进的IEEE 33节点算例进行仿真,仿真结果表明文中所采用的并行控制方式能有效提高优化求解的效率,验证了所提策略对多种分布式资源分级接入配电网运行调控具有指导意义。展开更多
Comparative analyses in ecology and evolution often face the challenge of controlling for the effects of shared ancestry(phylogeny)from those of ecological or trait-based predictors on species traits.Phylogenetic Gene...Comparative analyses in ecology and evolution often face the challenge of controlling for the effects of shared ancestry(phylogeny)from those of ecological or trait-based predictors on species traits.Phylogenetic Generalized Linear Models(PGLMs)address this issue by integrating phylogenetic relationships into statistical models.However,accurately partitioning explained variance among correlated predictors remains challenging.The phylolm.hp R package tackles this problem by extending the concept of“average shared variance”to PGLMs,enabling nuanced quantificationof the relative importance of phylogeny and other predictors.The package calculates individual likelihood-based R^(2) contributions of phylogeny and each predictor,accounting for both unique and shared explained variance.This approach overcomes limitations of traditional partial R^(2) methods,which often fail to sum the total R^(2) due to multicollinearity.We demonstrate the functionality of phylolm.hp through two case studies:one involving continuous trait data(maximum tree height in Californian species)and another focusing on binary trait data(species invasiveness in North American forests).The phylolm.hp package offers researchers a powerful tool to disentangle the contributions of phylogenetic and ecological predictors in comparative analyses.展开更多
In the internet protocol（IP） over multi-granular optical switch network （IP/MG-OXC）, the network node is a typical multilayer switch comprising several layers, the IP packet switching （PXC） layer, wavelength swi...In the internet protocol（IP） over multi-granular optical switch network （IP/MG-OXC）, the network node is a typical multilayer switch comprising several layers, the IP packet switching （PXC） layer, wavelength switching （WXC） layer and fiber switching （FXC） layer. This network is capable of both IP layer grooming and wavelength grooming in a hierarchical manner. Resource provisioning in the multi-granular network paradigm is called hierarchical grooming problem. An integer linear programming （ILP） model is proposed to formulate the problem. An iterative heuristic approach is developed for solving the problem in large networks. Case study shows that IP/MG-OXC network is much more extendible and can significantly save the overall network cost as compared with IP over wavelength division multiplexing network.展开更多
文摘In this paper, the authors propose a new approach to image compression based on the principle of Set Partitioning in Hierarchical Tree algorithm （SPIHT）. Our approach, the modified SPIHT （MSPIHT）, distributes entropy differently than SPIHT and also optimizes the coding. This approach can produce results that are a significant improvement on the Peak Signal-to-Noise Ratio （PSNR） and compression ratio obtained by SPIHT algorithm, without affecting the computing time. These results are also comparable with those obtained using the Embedded Zerotree Wavelet （EZW） and Joint Photographic Experts Group 2000 （JPG2） algorithms.
基金National Natural Science Foundation of China,Grant/Award Number:62276210Natural Science Basic Research Program of Shaanxi,Grant/Award Number:2022JM-380。
文摘Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization capabilities.To address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance.The AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence features.Compared with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP recognition.The comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.
基金financial support from “13th five-year plan” equipment pre-research project of China (41422010501)。
文摘Based on the general [Mo] equivalent criterion and d-electron orbital theory, a new ultrahigh-strength βtitanium alloy with eight major elements(Ti-4.5Al-6.5Mo-2Cr-2.6Nb-2Zr-2Sn-1V, TB17) for industrial applications was developed. An ingot of five tons was successfully melted by thrice vacuum consumable arc melting. The microstructure and elements partitioning of different conditions were investigated systematically. The results suggest that the hierarchical structures of micro-scale first α phase(αf), nano-scale secondary α phase(αs), and ultrafine FCC substructures can be tailored by solution plus aging(STA) heat treatment. The lateral and epitaxial growth of αfphase promotes the HCP-α to FCC substructure transformation with the help of elements partitioning during the aging process. Moreover, the element V, generally regarded as β stabilizer, is found to mainly concentrate in the Al-rich αfphase in this study probably due to its relatively lower content and the strong bonding energy of Al-V. The hierarchical structure has a strong interaction with dislocations, which contributes to achieve a superhigh strength of 1376 MPa.In addition, the plastic strain is partitioned in the multi-scale precipitates(such as the α and FCC substructures) and β matrix, resulting in a considerable plasticity. TEM observation demonstrates that high density entangled dislocations at interfaces and mechanical twins exist in the STA sample after tensile test. It can be deduced that both dislocation slipping and twinning mechanisms are present in this alloy.Therefore, TB17 alloy can serve as an excellent candidate for structural materials on aircrafts that require high strength and lightweight.
基金This work was supported in part by the National Key Basic Research and Development Program of China[grant number 2013CB733404]the National Natural Science Foundation of China[grant number 61271401],[grant number 91338113].
文摘Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images,which poses great challenges for subsequent applications.Therefore,an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time.In this work,we proposed a multi-scale hierarchical representation,i.e.binary partition tree,for analyzing large-scale UAV images.More precisely,we first obtained an initial partition of images by an oversegmentation algorithm,i.e.the simple linear iterative clustering.Next,we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships.Moreover,objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure.Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1129014311471066)+1 种基金Fundamental Research of Civil Aircraft(Grant No.MJ-F-2012-04)the Fundamental Research Funds for the Central Universities(Grant No.DUT15LK44)
文摘The truncated hierarchical B-spline basis has been proposed for adaptive data fitting and has already drawn a lot of attention in theory and applications. However the stability with respect to the Lp-norm, 1 ≤ p 〈 ∞, is not clear. In this paper, we consider the Lp stability of the truncated hierarchical B-spline basis, since the Lp stability is useful for curve and surface fitting, especially for least squares fitting. We prove that this basis is weakly Lp stable. This means that the associated constants to be considered in the stability analysis are at most of polynomial growth in the number of the hierarchy depth.
基金supported by National Key R&D Program of China(2019YFC1521102)the National Natural Science Foundation of China(61932003)Beijing Science and Technology Plan(Z221100007722004).
文摘Hierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels simultaneously.Existing methods tend to overlook that different image regions contribute differently to label prediction at different granularities,and also insufficiently consider relationships between the hierarchical multi-granularity labels.We introduce a sequence-to-sequence mechanism to overcome these two problems and propose a multi-granularity sequence generation(MGSG)approach for the hierarchical multi-granularity image classification task.Specifically,we introduce a transformer architecture to encode the image into visual representation sequences.Next,we traverse the taxonomic tree and organize the multi-granularity labels into sequences,and vectorize them and add positional information.The proposed multi-granularity sequence generation method builds a decoder that takes visual representation sequences and semantic label embedding as inputs,and outputs the predicted multi-granularity label sequence.The decoder models dependencies and correlations between multi-granularity labels through a masked multi-head self-attention mechanism,and relates visual information to the semantic label information through a crossmodality attention mechanism.In this way,the proposed method preserves the relationships between labels at different granularity levels and takes into account the influence of different image regions on labels with different granularities.Evaluations on six public benchmarks qualitatively and quantitatively demonstrate the advantages of the proposed method.Our project is available at https://github.com/liuxindazz/mgs.
基金the General Program of National Natural Science Foundation of China(62072049).
文摘The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns,hindering artificial intelligence from empowering mobile devices.Moreover,these data are not identically and independently distributed(Non-IID)caused by their different context,which will deteriorate the performance of the model.To address these issues,we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation,named ADC-DL,which learns a shared model by collecting the synthetic samples generated on each device.To tackle the heterogeneity of data distribution,we propose an entropy topsis comprehensive tiering model for hierarchical clustering,which distinguishes clients in terms of their data characteristics.Subsequently,synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation.The procedure of dataset condensation can be adjusted adaptively according to the tier of the client.Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.
文摘分布式资源大规模并网要求配电网的灵活调控能力不断增强,如何充分利用多层级灵活性资源协助系统运行成为目前亟待解决的问题。为此,文中提供一种支撑多种资源接入配电网的分级自治协同策略。首先,分析多层级下灵活性资源特性,对分布式资源出力采用概率模型以减少其不确定性因素影响。其次,构建主变-馈线-台区分层分区优化调度模型,台区层进行内部自治并将等值结果传递给馈线层,馈线层基于网络架构和资源运行特性进行区域划分,实现兼顾系统安全性和经济性的主配协同优化,并采用基于谱惩罚参数的自适应交替方向乘子法(spectral penalty parameter based adaptive alternating direction method of multipliers,SPPA-ADMM)进行求解。最后,选用改进的IEEE 33节点算例进行仿真,仿真结果表明文中所采用的并行控制方式能有效提高优化求解的效率,验证了所提策略对多种分布式资源分级接入配电网运行调控具有指导意义。
基金supported by the National Natural Science Foundation of China(32271551,32571954)National Key Research and Development Program of China(2023YFF0805800)the Metasequoia funding of Nanjing Forestry University.
文摘Comparative analyses in ecology and evolution often face the challenge of controlling for the effects of shared ancestry(phylogeny)from those of ecological or trait-based predictors on species traits.Phylogenetic Generalized Linear Models(PGLMs)address this issue by integrating phylogenetic relationships into statistical models.However,accurately partitioning explained variance among correlated predictors remains challenging.The phylolm.hp R package tackles this problem by extending the concept of“average shared variance”to PGLMs,enabling nuanced quantificationof the relative importance of phylogeny and other predictors.The package calculates individual likelihood-based R^(2) contributions of phylogeny and each predictor,accounting for both unique and shared explained variance.This approach overcomes limitations of traditional partial R^(2) methods,which often fail to sum the total R^(2) due to multicollinearity.We demonstrate the functionality of phylolm.hp through two case studies:one involving continuous trait data(maximum tree height in Californian species)and another focusing on binary trait data(species invasiveness in North American forests).The phylolm.hp package offers researchers a powerful tool to disentangle the contributions of phylogenetic and ecological predictors in comparative analyses.
基金Sponsored by Agency for Singapore Technology and Advance Research(RGM01/16)
文摘In the internet protocol（IP） over multi-granular optical switch network （IP/MG-OXC）, the network node is a typical multilayer switch comprising several layers, the IP packet switching （PXC） layer, wavelength switching （WXC） layer and fiber switching （FXC） layer. This network is capable of both IP layer grooming and wavelength grooming in a hierarchical manner. Resource provisioning in the multi-granular network paradigm is called hierarchical grooming problem. An integer linear programming （ILP） model is proposed to formulate the problem. An iterative heuristic approach is developed for solving the problem in large networks. Case study shows that IP/MG-OXC network is much more extendible and can significantly save the overall network cost as compared with IP over wavelength division multiplexing network.
