MAXIMAL FUNCTION CHARACTERIZATIONS OF HARDY SPACES ASSOCIATED WITH BOTH NON-NEGATIVE SELF-ADJOINT OPERATORS SATISFYING GAUSSIAN ESTIMATES AND BALL QUASI-BANACH FUNCTION SPACES

Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying some mild assumptions.Let HX,L(ℝ^(n))be the Hardy space associated with both X and L,which is defined by the Lusin area function related to the semigroup generated by L.In this article,the authors establish various maximal function characterizations of the Hardy space HX,L(ℝ^(n))and then apply these characterizations to obtain the solvability of the related Cauchy problem.These results have a wide range of generality and,in particular,the specific spaces X to which these results can be applied include the weighted space,the variable space,the mixed-norm space,the Orlicz space,the Orlicz-slice space,and the Morrey space.Moreover,the obtained maximal function characterizations of the mixed-norm Hardy space,the Orlicz-slice Hardy space,and the Morrey-Hardy space associated with L are completely new.

Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.

Non-Negative Adaptive Mechanism-Based Sliding Mode Control for Parallel Manipulators with Uncertainties

In this paper,a non-negative adaptive mechanism based on an adaptive nonsingular fast terminal sliding mode control strategy is proposed to have finite time and high-speed trajectory tracking for parallel manipulators with the existence of unknown bounded complex uncertainties and external disturbances.The proposed approach is a hybrid scheme of the online non-negative adaptive mechanism,tracking differentiator,and nonsingular fast terminal sliding mode control(NFTSMC).Based on the online non-negative adaptive mechanism,the proposed control can remove the assumption that the uncertainties and disturbances must be bounded for the NFTSMC controllers.The proposed controller has several advantages such as simple structure,easy implementation,rapid response,chattering-free,high precision,robustness,singularity avoidance,and finite-time convergence.Since all control parameters are online updated via tracking differentiator and non-negative adaptive law,the tracking control performance at high-speed motions can be better in real-time requirement and disturbance rejection ability.Finally,simulation results validate the effectiveness of the proposed method.

Bismut Ricci 平坦双扭曲积埃尔米特流形

设(M1,g)和(M2,h)是两个埃尔米特流形, 双扭曲积埃尔米特流形 (f2M1 × f1M2,G) 是赋予了扭曲积埃尔米特度量G= f22g + f12h的乘积流形M1 × M2,其中f1和f2分别是M1和M2上的正值光滑函数。 本文给出双扭曲积埃尔米特流形的Bismut联络、Bismut曲率、Bismut Ricci曲率和Bismut标量曲率的表达式,并得到双扭曲积埃尔米特流形 Bismut Ricci 平坦的充要条件,从而给出构造 Bismut Ricci 平坦埃尔米特流形的有效方法。

Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index.

Navigation Finsler metrics on a gradient Ricci soliton

In this paper,we study a class of Finsler metrics defined by a vector field on a gradient Ricci soliton.We obtain a necessary and sufficient condition for these Finsler metrics on a compact gradient Ricci soliton to be of isotropic S-curvature by establishing a new integral inequality.Then we determine the Ricci curvature of navigation Finsler metrics of isotropic S-curvature on a gradient Ricci soliton generalizing result only known in the case when such soliton is of Einstein type.As its application,we obtain the Ricci curvature of all navigation Finsler metrics of isotropic S-curvature on Gaussian shrinking soliton.

具有常数正Ricci曲率的图

本文在Lin-Lu-Yau给出的图的Ricci曲率的定义下,刻画了一类具有常数正Ricci曲率的图。更进一步地,本文找到了图上每条边的Ricci曲率都不小于1的充分必要条件,并刻画了图上每条边的Ricci曲率都等于1的图。

梯度Ricci-Yamabe孤立子的一些刚性结果

应用散度定理及一些Riemann流形上的重要不等式,并结合几何分析的方法研究紧致梯度Ricci-Yamabe孤立子的刚性问题,在适当的条件下得到非平凡紧致梯度Ricci-Yamabe孤立子与欧氏球面等距的刚性结果.此外,在数量曲率为正的假设下,证明满足L^(n/2)-积分拼挤条件的n(4≤n≤6)维紧致梯度收缩Ricci-Yamabe孤立子一定是Einstein流形.

Khler Manifolds with Almost Non-negative Ricci Curvature

Compact Kihler manifolds with semi-positive Ricci curvature have been inves- tigated by various authors.From Peternell's work,if M is a compact K■hler n-manifold with semi-positive Ricci curvature and finite fundamental group,then the universal cover has a decomposition ■≌X_1 x"'x X_m,where X_j is a Calabi-Yau manifold,or a hy- perKhler manifold,or X_j satisfies H^o(X_j,Ω~p)=O.The purpose of this paper is to generalize this theorem to almost non-negative Ricci curvature Khler manifolds by us- ing the Gromov-Hausdorff convergence.Let M be a compact complex n-manifold with non-vanishing Euler number.If for any ■>O,there exists a K■hler structure(J_e,g_e)on M such that the volume Vol_(ge)(M)<V,the sectional curvature|K(g_e)|<A^2,and the Ricci-tensor Ric(g_e)>-■g_e,where V and A are two constants independent of ■.Then the fundamental group of M is finite,and M is diffeornorphic to a complex manifold X such that the universal covering of X has a decomposition, ■≌X_1x...xX_s,where X_i is a Calabi-Yau manifold,or a hyperK■ihler manifold,or X_i satisfies H^o(X_i,Ω~P)={O},p>O.

一类三维非单模洛伦兹李群上的代数Ricci孤立子

Ricci孤立子是一类特殊的黎曼度量,类似于Ricci曲率定号流形,是近些年研究的热点。关于具有积结构的三维洛伦兹李群与两种联络有关的代数Ricci孤立子存在情况,有学者已经给出了明确的结论。本文在现有成果的基础上,将其拓展到具体一类三维非单模左不变洛伦兹李群上与三种联络相关的两类代数Ricci孤立子存在的情形,给出了该群分别与三种联络有关的两类代数Ricci孤立子存在条件的具体结果,这对揭示李群上几何性质和拓扑性质有重要的理论研究意义。

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

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.

Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation

This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.

Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration

This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.

Obtaining Profiles Based on Localized Non-negative Matrix Factorization

Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of information are usually redundancy. In this paper, we propose two different approaches based on localized non-negative matrix factorization (LNMF) to obtain the typical user session profiles and typical semantic profiles of junk mails. The LNMF get basis vectors as orthogonal as possible so that it can get accurate profiles. The experiments show that the approach based on LNMF can obtain better profiles than the approach based on NMF. Key words localized non-negative matrix factorization - profile - log mining - mail filtering CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024), National Grand Fundamental Research 973 Program of China (2002CB312000), National Research Foundation for the Doctoral Program of Higher Education of China (20020286004).Biography: Jiang Ji-xiang (1980-), male, Master candidate, research direction: data mining, knowledge representation on the Web.

High Quality Audio Object Coding Framework Based on Non-Negative Matrix Factorization

Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization(NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.

Application and Effect of Intelligent Disinfection Robot in Non-Negative Pressure Isolation Ward of Novel Coronavirus Pneumonia Designated Hospital

The application of intelligent disinfection robot in designated non-negative pressure isolation ward during the outbreak in novel coronavirus pneumonia. The construction and competition, disinfection mode of intelligent disinfection robot, the setting of disinfection point built on area and number of isolation ward, can be introduced below. Frequency can realize remote control when staff uses a table to give instruction and set disinfection mode, and then the intelligent disinfection robot returns automatically to the charging pile to charge when the instruction is completed. It can also autonomously move to sterilize without human participation, which makes man-machine separation and accurate disinfection come true. The chance of contact infection and exposure is decreased when an intelligent disinfection robot is used to sterilize the environment and object surface in an isolation ward, which can also reduce occupational exposure, achieve occupational protection of medical workers and ensure there is no hospital infection.

弦σ模型中的Ricci流扰动和Weyl反常系数

研究d=2维时空中玻色弦σ模型,采用2种Ricci流扰动方程推导Weyl反常系数.第1种方程是引力子场Gμν\,Dilaton场Φ的双圈流方程;第2种方程是引力子场G_(μν),轴子场B_(μν)和Dilaton场Φ的单圈流方程,通过这些Ricci流方程导出引力场的扰动方程.另外,还导出Weyl反常系数β_(μν)^(G),β_(μν)^(B),β^(Φ)的表达式,分析和讨论这些β函数随动量标度λ变化的物理意义.

调和Ricci流下热方程的梯度估计及应用

在度量满足调和Ricci流的闭黎曼流形上,利用抛物型方程的极值原理证明热方程正解的一个梯度估计.结合索伯列夫不等式和指数加权法,进一步得到共轭热方程基本解的高斯型上界.

A novel trilinear decomposition algorithm:Three-dimension non-negative matrix factorization

Non-negative matrix factorization （NMF） is a technique for dimensionality reduction by placing non-negativity constraints on the matrix. Based on the PARAFAC model, NMF was extended for three-dimension data decomposition. The three-dimension nonnegative matrix factorization （NMF3） algorithm, which was concise and easy to implement, was given in this paper. The NMF3 algorithm implementation was based on elements but not on vectors. It could decompose a data array directly without unfolding, which was not similar to that the traditional algorithms do, It has been applied to the simulated data array decomposition and obtained reasonable results. It showed that NMF3 could be introduced for curve resolution in chemometrics.

ISAR target recognition based on non-negative sparse coding

Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.