Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-...Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.展开更多
Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data ...Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles.Hourly speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct study.The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,respectively.Alkanes and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite tendency.The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly small.The reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses.Photochemical losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high reactivities.Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its profile.Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles.This study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty.展开更多
Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were freque...Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.展开更多
CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs a...CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis,treatment,and disease prevention.However,existing models have limitations in accurately capturing similarities,handling the sparse and noise attributes of association networks,and fully leveraging bioinformatical aspects from multiple viewpoints.To address these issues,this study introduces a new non-negative matrix factorization-based framework called NMFMSN.First,we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity,respectively.Given the sparse known associations between circRNAs and diseases,we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions.Finally,we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations.Upon conducting 5-fold cross-validation and leave-one-out cross-validation,the AUC values for NMFMSN reach 0.9712 and 0.9768,respectively,outperforming the currently most advanced models.Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases.展开更多
In this paper,a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed.The actuator is limited by upper and l...In this paper,a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed.The actuator is limited by upper and lower constraints and the sensor in the feedback loop is subjected to network-induced unknown time-varying delay and noise.With this nonlinear control method,we first employ right coprime factorization based on isomorphism and operator theory to factorize the plant,so that bounded input bounded output(BIBO)stability can be guaranteed.Next,continuous-time generalized predictive control(CGPC)is utilized for the unstable operator of the right coprime factorized plant to guarantee inner stability and enables the closed-loop dynamics of the system with predictive characteristics.Meanwhile,a second-Do F(degrees of freedom)switched controller that satisfies a perturbed Bezout identity and a robustness condition is designed.By using the CGPC controller that possesses predictive behavior and the second-Do F switched stabilizer,the overall stability of the plant subjected to actuator limitations is guaranteed.To address sensor limitations that exist in networked plants in the form of delay and noise which often cause system performance degradation,we implement an identity operator definition in the feedback loop to compensate for these adverse effects.Further,a pre-operator is designed to ensure that the plant output tracks the reference input.Finally,the effectiveness of the proposed design scheme is demonstrated by simulations.展开更多
Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and...Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).展开更多
This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed ...This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.展开更多
The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal ac...The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.展开更多
Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of ...Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods.To address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data.Additionally,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters apart.Extensive experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.展开更多
One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading.Most of the current centrality algorithms...One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading.Most of the current centrality algorithms focus on single feature or manually extract the attributes,which occasionally results in the failure to fully capture the vertex’s importance.A new vertex centrality approach based on symmetric nonnegative matrix factorization(SNMF),called VCSNMF,is proposed in this paper.For highlight the characteristics of a network,the adjacency matrix and the degree matrix are fused to represent original data of the network via a weighted linear combination.First,SNMF automatically extracts the latent characteristics of vertices by factorizing the established original data matrix.Then we prove that each vertex’s composite feature which is constructed with one-dimensional factor matrix can be approximated as the term of eigenvector associated with the spectral radius of the network,otherwise obtained by the factor matrix on the hyperspace.Finally,VCSNMF integrates the composite feature and the topological structure to evaluate the performance of vertices.To verify the effectiveness of the VCSNMF criterion,eight existing centrality approaches are used as comparison measures to rank influential vertices in ten real-world networks.The experimental results assert the superiority of the method.展开更多
This paper considers the updating problem of the hyperbolic matrix factorizations. The sufficient conditions for the existence of the updated hyperbolic matrix factorizations are first provided. Then, some differentia...This paper considers the updating problem of the hyperbolic matrix factorizations. The sufficient conditions for the existence of the updated hyperbolic matrix factorizations are first provided. Then, some differential inequalities and first order perturbation expansions for the updated hyperbolic factors are derived. These results generalize the corresponding ones for the updating problem of the classical QR factorization obtained by Jiguang SUN.展开更多
Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is propose...Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is proposed. First, a non-negative tensor factorization（NTF） algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images＇ weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.展开更多
Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective ...Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective function theory to non- negative tensor factorization and combining the three semi-non- negative matrix factorization（NMF） model. The effectiveness of the method is verified by the facial feature extraction experiment. Through the decomposition of a series of an air compressor＇s vibration signals composed in the form of a bispectrum by this new method, the basis images representing the fault features and corresponding weight matrices are obtained. Then the relationships between characteristics and faults are analyzed and the fault types are classified by importing the weight matrices into the BP neural network. Experimental results show that the accuracy of fault diagnosis is improved by this new method compared with other feature extraction methods.展开更多
We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute t...We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute the solution by solving several triangular systems. We give the first order error analysis to show that the method is backward stable. The method is more efficient than the backward stable method proposed by Chandrasekaran, Gu and Sayed.展开更多
Let G be a graph, k(1), ... , k(m) be positive integers. If the edges of graph G can be decomposed into some edge disjoint [0, k(1)]-factor F-1, ..., [0, k(m)]-factor F-m, then we can say (F) over bar = {F-1, ..., F-m...Let G be a graph, k(1), ... , k(m) be positive integers. If the edges of graph G can be decomposed into some edge disjoint [0, k(1)]-factor F-1, ..., [0, k(m)]-factor F-m, then we can say (F) over bar = {F-1, ..., F-m}, is a [0, k(i)](1)(m) -factorization of G. If H is a subgraph with m edges in graph G and / E (H) boolean AND E(F-i) / = 1 for all 1 less than or equal to i less than or equal to m, then we can call that (F) over bar is orthogonal to H. It is proved that if G is a [0, k(1) + ... + k(m) - m + 1]-graph, H is a subgraph with m edges in G, then graph G has a [0, k(i)](1)(m)-factorization orthogonal to H.展开更多
In actual engineering, processing of big data sometimes requires building of mass physical models, while processing of physical model requires relevant math model, thus producing mass multivariate polynomials, the eff...In actual engineering, processing of big data sometimes requires building of mass physical models, while processing of physical model requires relevant math model, thus producing mass multivariate polynomials, the effective reduction of which is a difficult problem at present. A novel algorithm is proposed to achieve the approximation factorization of complex coefficient multivariate polynomial in light of characteristics of multivariate polynomials. At first, the multivariate polynomial is reduced to be the binary polynomial, then the approximation factorization of binary polynomial can produce irreducible duality factor, at last, the irreducible duality factor is restored to the irreducible multiple factor. As a unit root is cyclic, selecting the unit root as the reduced factor can ensure the coefficient does not expand in a reduction process. Chinese remainder theorem is adopted in the corresponding reduction process, which brought down the calculation complexity. The algorithm is based on approximation factorization of binary polynomial and calculation of approximation Greatest Common Divisor, GCD. The algorithm can solve the reduction of multivariate polynomials in massive math models, which can obtain effectively null point of multivariate polynomials, providing a new approach for further analysis and explanation of physical models. The experiment result shows that the irreducible factors from this method get close to the real factors with high efficiency.展开更多
A multi-qubit pure quantum state is called separable when it can be factored as the tensor product of 1-qubit pure quantum states.Factorizing a general multi-qubit pure quantum state into the tensor product of its fac...A multi-qubit pure quantum state is called separable when it can be factored as the tensor product of 1-qubit pure quantum states.Factorizing a general multi-qubit pure quantum state into the tensor product of its factors(pure states containing a smaller number of qubits)can be a challenging task,especially for highly entangled states.A new criterion based on the proportionality of the rows of certain associated matrices for the existence of certain factorization and a factorization algorithm that follows from this criterion for systematically extracting all the factors is developed in this paper.3-qubit pure states play a crucial role in quantum computing and quantum information processing.For various applications,the well-known 3-qubit GHZ state which contains two nonzero terms,and the 3-qubit W state which contains three nonzero terms,have been studied extensively.Using the new factorization algorithm developed here we perform a complete analysis vis-à-vis entanglement of 3-qubit states that contain exactly two nonzero terms and exactly three nonzero terms.展开更多
Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smar...Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.展开更多
Let Sn be the star with n vertices, and let G be any connected graph with p vertices. We denote by Eτp＋（r-1）^G（i） the graph obtained from Sr and rG by coinciding the i-th vertex of G with the vertex of degree r ...Let Sn be the star with n vertices, and let G be any connected graph with p vertices. We denote by Eτp＋（r-1）^G（i） the graph obtained from Sr and rG by coinciding the i-th vertex of G with the vertex of degree r - 1 of S,, while the i-th vertex of each component of （r - 1）G be adjacented to r - 1 vertices of degree 1 of St, respectively. By applying the properties of adjoint polynomials, We prove that factorization theorem of adjoint polynomials of kinds of graphs Eτp＋（r-1）^G（i）∪（r - 1）K1 （1 ≤i≤p）. Furthermore, we obtain structure characteristics of chromatically equivalent graphs of their complements.展开更多
In order to protect copyright of digital images,a new robust digital image watermarking algorithm based on chaotic system and QR factorization was proposed.The host images were firstly divided into blocks with same si...In order to protect copyright of digital images,a new robust digital image watermarking algorithm based on chaotic system and QR factorization was proposed.The host images were firstly divided into blocks with same size,then QR factorization was performed on each block.Pseudorandom circular chain(PCC) generated by logistic mapping(LM) was applied to select the embedding blocks for enhancing the security of the scheme.The first column coefficients in Q matrix of chosen blocks were modified to embed watermarks without causing noticeable artifacts.Watermark extraction procedure was performed without the original cover image.The experimental results demonstrate that the watermarked images have good visual quality and this scheme is better than the existing techniques,especially when the image is attacked by cropping,noise pollution and so on.Analysis and discussion on robustness and security issues were also presented.展开更多
基金supported by the National Natural Science Foundation of China(62272078)Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300210)
文摘Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.
基金supported by the National Key R&D Program of China(No.2023YFC3705801)the National Natural Science Foundation of China(No.42177085).
文摘Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles.Hourly speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct study.The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,respectively.Alkanes and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite tendency.The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly small.The reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses.Photochemical losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high reactivities.Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its profile.Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles.This study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty.
基金supported by the National Institute of Environmental Research(NIER)funded by the Ministry of Environment(No.NIER-2019-04-02-039)supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry&Technology Institute(KEITI)funded by the Ministry of Environment(MOE).
文摘Fine particulatematter(PM_(2.5))samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM_(2.5) in which the average mass concentration exceeded 75μg/m^(3) were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM_(2.5) as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM_(2.5) sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities.
基金the Gansu Province Industrial Support Plan(No.2023CYZC-25)Natural Science Foundation of Gansu Province(No.23JRRA770)the National Natural Science Foundation of China(No.62162040)。
文摘CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis,treatment,and disease prevention.However,existing models have limitations in accurately capturing similarities,handling the sparse and noise attributes of association networks,and fully leveraging bioinformatical aspects from multiple viewpoints.To address these issues,this study introduces a new non-negative matrix factorization-based framework called NMFMSN.First,we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity,respectively.Given the sparse known associations between circRNAs and diseases,we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions.Finally,we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations.Upon conducting 5-fold cross-validation and leave-one-out cross-validation,the AUC values for NMFMSN reach 0.9712 and 0.9768,respectively,outperforming the currently most advanced models.Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases.
文摘In this paper,a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed.The actuator is limited by upper and lower constraints and the sensor in the feedback loop is subjected to network-induced unknown time-varying delay and noise.With this nonlinear control method,we first employ right coprime factorization based on isomorphism and operator theory to factorize the plant,so that bounded input bounded output(BIBO)stability can be guaranteed.Next,continuous-time generalized predictive control(CGPC)is utilized for the unstable operator of the right coprime factorized plant to guarantee inner stability and enables the closed-loop dynamics of the system with predictive characteristics.Meanwhile,a second-Do F(degrees of freedom)switched controller that satisfies a perturbed Bezout identity and a robustness condition is designed.By using the CGPC controller that possesses predictive behavior and the second-Do F switched stabilizer,the overall stability of the plant subjected to actuator limitations is guaranteed.To address sensor limitations that exist in networked plants in the form of delay and noise which often cause system performance degradation,we implement an identity operator definition in the feedback loop to compensate for these adverse effects.Further,a pre-operator is designed to ensure that the plant output tracks the reference input.Finally,the effectiveness of the proposed design scheme is demonstrated by simulations.
文摘Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).
基金supported by Project of Chongqing Science and Technology Bureau (cstc2022jxjl0005)。
文摘This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.
基金This research work has been conducted in cooperation with members of DETSI project supported by BPI France and Pays de Loire and Auvergne Rhone Alpes.
文摘The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.
基金supported by the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2023GXJS163,ZDYF2024GXJS014)National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024)+3 种基金the Major Science and Technology Project of Hainan Province(Grant No.ZDKJ2020012)Hainan Provincial Natural Science Foundation of China(Grant No.620MS021)Youth Foundation Project of Hainan Natural Science Foundation(621QN211)Innovative Research Project for Graduate Students in Hainan Province(Grant Nos.Qhys2023-96,Qhys2023-95).
文摘Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods.To address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data.Additionally,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters apart.Extensive experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.
基金the National Natural Science Foundation of China(Nos.11361033 and 11861045)。
文摘One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading.Most of the current centrality algorithms focus on single feature or manually extract the attributes,which occasionally results in the failure to fully capture the vertex’s importance.A new vertex centrality approach based on symmetric nonnegative matrix factorization(SNMF),called VCSNMF,is proposed in this paper.For highlight the characteristics of a network,the adjacency matrix and the degree matrix are fused to represent original data of the network via a weighted linear combination.First,SNMF automatically extracts the latent characteristics of vertices by factorizing the established original data matrix.Then we prove that each vertex’s composite feature which is constructed with one-dimensional factor matrix can be approximated as the term of eigenvector associated with the spectral radius of the network,otherwise obtained by the factor matrix on the hyperspace.Finally,VCSNMF integrates the composite feature and the topological structure to evaluate the performance of vertices.To verify the effectiveness of the VCSNMF criterion,eight existing centrality approaches are used as comparison measures to rank influential vertices in ten real-world networks.The experimental results assert the superiority of the method.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1120150711171361)the Natural Science Foundation Project of CQ CSTC(Grant No.2010BB9215)
文摘This paper considers the updating problem of the hyperbolic matrix factorizations. The sufficient conditions for the existence of the updated hyperbolic matrix factorizations are first provided. Then, some differential inequalities and first order perturbation expansions for the updated hyperbolic factors are derived. These results generalize the corresponding ones for the updating problem of the classical QR factorization obtained by Jiguang SUN.
基金The National Natural Science Foundation of China (No.50875048)the Natural Science Foundation of Jiangsu Province (No.BK2007115)the National High Technology Research and Development Program of China (863 Program)(No.2007AA04Z421)
文摘Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is proposed. First, a non-negative tensor factorization（NTF） algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images＇ weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.
基金The National Natural Science Foundation of China(No.50875078)the Natural Science Foundation of Jiangsu Province(No.BK2007115)the National High Technology Research and Development Program of China(863 Program)(No.2007AA04Z421)
文摘Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective function theory to non- negative tensor factorization and combining the three semi-non- negative matrix factorization（NMF） model. The effectiveness of the method is verified by the facial feature extraction experiment. Through the decomposition of a series of an air compressor＇s vibration signals composed in the form of a bispectrum by this new method, the basis images representing the fault features and corresponding weight matrices are obtained. Then the relationships between characteristics and faults are analyzed and the fault types are classified by importing the weight matrices into the BP neural network. Experimental results show that the accuracy of fault diagnosis is improved by this new method compared with other feature extraction methods.
文摘We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute the solution by solving several triangular systems. We give the first order error analysis to show that the method is backward stable. The method is more efficient than the backward stable method proposed by Chandrasekaran, Gu and Sayed.
文摘Let G be a graph, k(1), ... , k(m) be positive integers. If the edges of graph G can be decomposed into some edge disjoint [0, k(1)]-factor F-1, ..., [0, k(m)]-factor F-m, then we can say (F) over bar = {F-1, ..., F-m}, is a [0, k(i)](1)(m) -factorization of G. If H is a subgraph with m edges in graph G and / E (H) boolean AND E(F-i) / = 1 for all 1 less than or equal to i less than or equal to m, then we can call that (F) over bar is orthogonal to H. It is proved that if G is a [0, k(1) + ... + k(m) - m + 1]-graph, H is a subgraph with m edges in G, then graph G has a [0, k(i)](1)(m)-factorization orthogonal to H.
文摘In actual engineering, processing of big data sometimes requires building of mass physical models, while processing of physical model requires relevant math model, thus producing mass multivariate polynomials, the effective reduction of which is a difficult problem at present. A novel algorithm is proposed to achieve the approximation factorization of complex coefficient multivariate polynomial in light of characteristics of multivariate polynomials. At first, the multivariate polynomial is reduced to be the binary polynomial, then the approximation factorization of binary polynomial can produce irreducible duality factor, at last, the irreducible duality factor is restored to the irreducible multiple factor. As a unit root is cyclic, selecting the unit root as the reduced factor can ensure the coefficient does not expand in a reduction process. Chinese remainder theorem is adopted in the corresponding reduction process, which brought down the calculation complexity. The algorithm is based on approximation factorization of binary polynomial and calculation of approximation Greatest Common Divisor, GCD. The algorithm can solve the reduction of multivariate polynomials in massive math models, which can obtain effectively null point of multivariate polynomials, providing a new approach for further analysis and explanation of physical models. The experiment result shows that the irreducible factors from this method get close to the real factors with high efficiency.
文摘A multi-qubit pure quantum state is called separable when it can be factored as the tensor product of 1-qubit pure quantum states.Factorizing a general multi-qubit pure quantum state into the tensor product of its factors(pure states containing a smaller number of qubits)can be a challenging task,especially for highly entangled states.A new criterion based on the proportionality of the rows of certain associated matrices for the existence of certain factorization and a factorization algorithm that follows from this criterion for systematically extracting all the factors is developed in this paper.3-qubit pure states play a crucial role in quantum computing and quantum information processing.For various applications,the well-known 3-qubit GHZ state which contains two nonzero terms,and the 3-qubit W state which contains three nonzero terms,have been studied extensively.Using the new factorization algorithm developed here we perform a complete analysis vis-à-vis entanglement of 3-qubit states that contain exactly two nonzero terms and exactly three nonzero terms.
基金Supported by Shaanxi Provincial Overall Innovation Project of Science and Technology,China(Grant No.2013KTCQ01-06)
文摘Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.
文摘Let Sn be the star with n vertices, and let G be any connected graph with p vertices. We denote by Eτp＋（r-1）^G（i） the graph obtained from Sr and rG by coinciding the i-th vertex of G with the vertex of degree r - 1 of S,, while the i-th vertex of each component of （r - 1）G be adjacented to r - 1 vertices of degree 1 of St, respectively. By applying the properties of adjoint polynomials, We prove that factorization theorem of adjoint polynomials of kinds of graphs Eτp＋（r-1）^G（i）∪（r - 1）K1 （1 ≤i≤p）. Furthermore, we obtain structure characteristics of chromatically equivalent graphs of their complements.
基金Project(2007AA01Z241-2) supported by the National High-tech Research and Development Program of ChinaProject(2006XM002) supported by Beijing Jiaotong University Science Foundation,ChinaProject(0910KYZY55) supported by the Fundamental Research Funds for the Central University in China
文摘In order to protect copyright of digital images,a new robust digital image watermarking algorithm based on chaotic system and QR factorization was proposed.The host images were firstly divided into blocks with same size,then QR factorization was performed on each block.Pseudorandom circular chain(PCC) generated by logistic mapping(LM) was applied to select the embedding blocks for enhancing the security of the scheme.The first column coefficients in Q matrix of chosen blocks were modified to embed watermarks without causing noticeable artifacts.Watermark extraction procedure was performed without the original cover image.The experimental results demonstrate that the watermarked images have good visual quality and this scheme is better than the existing techniques,especially when the image is attacked by cropping,noise pollution and so on.Analysis and discussion on robustness and security issues were also presented.
