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
Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin s...Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).展开更多
Yinshan anticline is the product of tectono-dynamic deformation - metamorphism .Along the axis of the anticline exists a brittle-ductile shearing zone which obviously controls the ore-formation . Mineralization occurs...Yinshan anticline is the product of tectono-dynamic deformation - metamorphism .Along the axis of the anticline exists a brittle-ductile shearing zone which obviously controls the ore-formation . Mineralization occurs along the axis of the anticline in a width of about 1000m .In the mining area .volcano- subvolcanic rocks of Early Yanshan period are divided into three cycles :Ⅰ intermediate acidic dacite lava and dacite porphyry ;Ⅱ acidic amphibole liparite and quartz porphyry;Ⅲ intermediate andesite porphyrite . Among them activities of ⅠandⅡ cycles are more intensive and are intimately related to the mineralization . Yinshan ore deposit is the result of combinative processes of tectono -dynamic and volcano -magmatic hydrothermal fluids, so that mere are two centers of metallogenic zoning, one being the axial strain zone of Yinshan anticline which is the center of first order, and the other being porphyry stock , 2nd order.展开更多
By applying the convolution technique to the treatment of oscillographic signal,a new electroanalytical method,0.5-3.5 order differential A.C.oscillographic chronopotentiometry is presented.This note represents the ex...By applying the convolution technique to the treatment of oscillographic signal,a new electroanalytical method,0.5-3.5 order differential A.C.oscillographic chronopotentiometry is presented.This note represents the experimental circuits,principle and characteristics of the method.展开更多
In this paper, we apply the Legendre spectral-collocation method to obtain approximate solutions of nonlinear multi-order fractional differential equations (M-FDEs). The fractional derivative is described in the Caput...In this paper, we apply the Legendre spectral-collocation method to obtain approximate solutions of nonlinear multi-order fractional differential equations (M-FDEs). The fractional derivative is described in the Caputo sense. The study is conducted through illustrative example to demonstrate the validity and applicability of the presented method. The results reveal that the proposed method is very effective and simple. Moreover, only a small number of shifted Legendre polynomials are needed to obtain a satisfactory result.展开更多
For the model of a Closed Phase Locked Loop (CPLL) communication System consists of both the transmission and receiver ends. This model is considered to be in a multi-order intermittent chaotic state. The chaotic sign...For the model of a Closed Phase Locked Loop (CPLL) communication System consists of both the transmission and receiver ends. This model is considered to be in a multi-order intermittent chaotic state. The chaotic signals are then synchronized along side with our system. This chaotic synchronization will be demonstrated and furthermore, a modulation will be formed to examine the system if it will perfectly reconstruct or not. Finally we will demonstrate the synchronization conditions of the system.展开更多
为解决快速路高架路段大间距出口标志信息供需失衡问题,借助眼动追踪技术开展室内试验。设计77种不同标志,提取并分析28种眼动数据指标,从视认效率、难度、疲劳三个维度构建评价体系。运用重复测量方差分析与逼近理想解排序法(Technique...为解决快速路高架路段大间距出口标志信息供需失衡问题,借助眼动追踪技术开展室内试验。设计77种不同标志,提取并分析28种眼动数据指标,从视认效率、难度、疲劳三个维度构建评价体系。运用重复测量方差分析与逼近理想解排序法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS),探究多信息标志多维视认特性。结果表明,28种眼动指标能有效评估驾驶员视认特性,精准表征视认微观特征。标志中路名数量越多,驾驶员视认效率越低、难度越大、疲劳程度越高,整体视认风险增加。建议标志信息数量一般不超过6条,特殊情况可扩展至9条。此外,目的路名在版面的位置对视认风险影响显著,应合理设置版面优先级。展开更多
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.
基金supported by the National Key R&D Program of China(2023YFC3304600).
文摘Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).
文摘Yinshan anticline is the product of tectono-dynamic deformation - metamorphism .Along the axis of the anticline exists a brittle-ductile shearing zone which obviously controls the ore-formation . Mineralization occurs along the axis of the anticline in a width of about 1000m .In the mining area .volcano- subvolcanic rocks of Early Yanshan period are divided into three cycles :Ⅰ intermediate acidic dacite lava and dacite porphyry ;Ⅱ acidic amphibole liparite and quartz porphyry;Ⅲ intermediate andesite porphyrite . Among them activities of ⅠandⅡ cycles are more intensive and are intimately related to the mineralization . Yinshan ore deposit is the result of combinative processes of tectono -dynamic and volcano -magmatic hydrothermal fluids, so that mere are two centers of metallogenic zoning, one being the axial strain zone of Yinshan anticline which is the center of first order, and the other being porphyry stock , 2nd order.
基金supported by the National Natural Science Foundation of Chinaby the Special Subject Foundation of Educational Committee of Shaanxi Province.
文摘By applying the convolution technique to the treatment of oscillographic signal,a new electroanalytical method,0.5-3.5 order differential A.C.oscillographic chronopotentiometry is presented.This note represents the experimental circuits,principle and characteristics of the method.
文摘In this paper, we apply the Legendre spectral-collocation method to obtain approximate solutions of nonlinear multi-order fractional differential equations (M-FDEs). The fractional derivative is described in the Caputo sense. The study is conducted through illustrative example to demonstrate the validity and applicability of the presented method. The results reveal that the proposed method is very effective and simple. Moreover, only a small number of shifted Legendre polynomials are needed to obtain a satisfactory result.
文摘For the model of a Closed Phase Locked Loop (CPLL) communication System consists of both the transmission and receiver ends. This model is considered to be in a multi-order intermittent chaotic state. The chaotic signals are then synchronized along side with our system. This chaotic synchronization will be demonstrated and furthermore, a modulation will be formed to examine the system if it will perfectly reconstruct or not. Finally we will demonstrate the synchronization conditions of the system.
文摘为解决快速路高架路段大间距出口标志信息供需失衡问题,借助眼动追踪技术开展室内试验。设计77种不同标志,提取并分析28种眼动数据指标,从视认效率、难度、疲劳三个维度构建评价体系。运用重复测量方差分析与逼近理想解排序法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS),探究多信息标志多维视认特性。结果表明,28种眼动指标能有效评估驾驶员视认特性,精准表征视认微观特征。标志中路名数量越多,驾驶员视认效率越低、难度越大、疲劳程度越高,整体视认风险增加。建议标志信息数量一般不超过6条,特殊情况可扩展至9条。此外,目的路名在版面的位置对视认风险影响显著,应合理设置版面优先级。
