Computing a Nonnegative Matrix Factorization -- Provably
Arora, Sanjeev; Kannan, Ravi; Moitra, Ankur
2011-01-01
In the Nonnegative Matrix Factorization (NMF) problem we are given an $n \\times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express $M$ as $A W$ where $A$ and $W$ are nonnegative matrices of size $n \\times r$ and $r \\times m$ respectively. In some applications, it makes sense to ask instead for the product $AW$ to approximate $M$ -- i.e. (approximately) minimize $\
Max–min distance nonnegative matrix factorization
Wang, Jim Jing-Yan
2014-10-26
Nonnegative Matrix Factorization (NMF) has been a popular representation method for pattern classification problems. It tries to decompose a nonnegative matrix of data samples as the product of a nonnegative basis matrix and a nonnegative coefficient matrix. The columns of the coefficient matrix can be used as new representations of these data samples. However, traditional NMF methods ignore class labels of the data samples. In this paper, we propose a novel supervised NMF algorithm to improve the discriminative ability of the new representation by using the class labels. Using the class labels, we separate all the data sample pairs into within-class pairs and between-class pairs. To improve the discriminative ability of the new NMF representations, we propose to minimize the maximum distance of the within-class pairs in the new NMF space, and meanwhile to maximize the minimum distance of the between-class pairs. With this criterion, we construct an objective function and optimize it with regard to basis and coefficient matrices, and slack variables alternatively, resulting in an iterative algorithm. The proposed algorithm is evaluated on three pattern classification problems and experiment results show that it outperforms the state-of-the-art supervised NMF methods.
Non-negative Matrix Factorization for Binary Data
DEFF Research Database (Denmark)
Larsen, Jacob Søgaard; Clemmensen, Line Katrine Harder
We propose the Logistic Non-negative Matrix Factorization for decomposition of binary data. Binary data are frequently generated in e.g. text analysis, sensory data, market basket data etc. A common method for analysing non-negative data is the Non-negative Matrix Factorization, though this is in......We propose the Logistic Non-negative Matrix Factorization for decomposition of binary data. Binary data are frequently generated in e.g. text analysis, sensory data, market basket data etc. A common method for analysing non-negative data is the Non-negative Matrix Factorization, though...
On affine non-negative matrix factorization
DEFF Research Database (Denmark)
Laurberg, Hans; Hansen, Lars Kai
2007-01-01
We generalize the non-negative matrix factorization (NMF) generative model to incorporate an explicit offset. Multiplicative estimation algorithms are provided for the resulting sparse affine NMF model. We show that the affine model has improved uniqueness properties and leads to more accurate...
Nonnegative Matrix Factorizations Performing Object Detection and Localization
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G. Casalino
2012-01-01
Full Text Available We study the problem of detecting and localizing objects in still, gray-scale images making use of the part-based representation provided by nonnegative matrix factorizations. Nonnegative matrix factorization represents an emerging example of subspace methods, which is able to extract interpretable parts from a set of template image objects and then to additively use them for describing individual objects. In this paper, we present a prototype system based on some nonnegative factorization algorithms, which differ in the additional properties added to the nonnegative representation of data, in order to investigate if any additional constraint produces better results in general object detection via nonnegative matrix factorizations.
An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors
Xu, Yangyang; Wen, Zaiwen; Zhang, Yin
2011-01-01
This paper introduces a novel algorithm for the nonnegative matrix factorization and completion problem, which aims to find nonnegative matrices X and Y from a subset of entries of a nonnegative matrix M so that XY approximates M. This problem is closely related to the two existing problems: nonnegative matrix factorization and low-rank matrix completion, in the sense that it kills two birds with one stone. As it takes advantages of both nonnegativity and low rank, its results can be superior than those of the two problems alone. Our algorithm is applied to minimizing a non-convex constrained least-squares formulation and is based on the classic alternating direction augmented Lagrangian method. Preliminary convergence properties and numerical simulation results are presented. Compared to a recent algorithm for nonnegative random matrix factorization, the proposed algorithm yields comparable factorization through accessing only half of the matrix entries. On tasks of recovering incomplete grayscale and hypers...
Incremental Nonnegative Matrix Factorization for Face Recognition
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Wen-Sheng Chen
2008-01-01
Full Text Available Nonnegative matrix factorization (NMF is a promising approach for local feature extraction in face recognition tasks. However, there are two major drawbacks in almost all existing NMF-based methods. One shortcoming is that the computational cost is expensive for large matrix decomposition. The other is that it must conduct repetitive learning, when the training samples or classes are updated. To overcome these two limitations, this paper proposes a novel incremental nonnegative matrix factorization (INMF for face representation and recognition. The proposed INMF approach is based on a novel constraint criterion and our previous block strategy. It thus has some good properties, such as low computational complexity, sparse coefficient matrix. Also, the coefficient column vectors between different classes are orthogonal. In particular, it can be applied to incremental learning. Two face databases, namely FERET and CMU PIE face databases, are selected for evaluation. Compared with PCA and some state-of-the-art NMF-based methods, our INMF approach gives the best performance.
Multiple graph regularized nonnegative matrix factorization
Wang, Jim Jing-Yan
2013-10-01
Non-negative matrix factorization (NMF) has been widely used as a data representation method based on components. To overcome the disadvantage of NMF in failing to consider the manifold structure of a data set, graph regularized NMF (GrNMF) has been proposed by Cai et al. by constructing an affinity graph and searching for a matrix factorization that respects graph structure. Selecting a graph model and its corresponding parameters is critical for this strategy. This process is usually carried out by cross-validation or discrete grid search, which are time consuming and prone to overfitting. In this paper, we propose a GrNMF, called MultiGrNMF, in which the intrinsic manifold is approximated by a linear combination of several graphs with different models and parameters inspired by ensemble manifold regularization. Factorization metrics and linear combination coefficients of graphs are determined simultaneously within a unified object function. They are alternately optimized in an iterative algorithm, thus resulting in a novel data representation algorithm. Extensive experiments on a protein subcellular localization task and an Alzheimer\\'s disease diagnosis task demonstrate the effectiveness of the proposed algorithm. © 2013 Elsevier Ltd. All rights reserved.
THE SENSITIVITY OF THE EXPONENTIAL OF AN ESSENTIALLY NONNEGATIVE MATRIX
Institute of Scientific and Technical Information of China (English)
Weifang Zhu; Jungong Xue; Weiguo Gao
2008-01-01
This paper performs perturbation analysis for the exponential of an essentially nonnegative matrix which is perturbed in the way that each entry has a small relative perturbation.For a general essentially nonnegative matrix,we obtain an upper bound for the relative error in 2-norm,which is sharper than the existing perturbation results.For a triangular essentially nonnegative matrix,we obtain an upper bound for the relative error in entrywise sense.This bound indicates that,if the spectral radius of an essentially nonnegative matrix is not large,then small entrywise relative perturbations cause small relative error in each entry of its exponential.Finally,we apply our perturbation results to the sensitivity analysis of RC networks and complementary distribution functions of phase-type distributions.
Non-negative matrix factorization with Gaussian process priors
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Laurberg, Hans
2008-01-01
We present a general method for including prior knowledge in a nonnegative matrix factorization (NMF), based on Gaussian process priors. We assume that the nonnegative factors in the NMF are linked by a strictly increasing function to an underlying Gaussian process specified by its covariance...... function. This allows us to find NMF decompositions that agree with our prior knowledge of the distribution of the factors, such as sparseness, smoothness, and symmetries. The method is demonstrated with an example from chemical shift brain imaging....
Non-negative matrix factorization with Gaussian process priors
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Laurberg, Hans
2008-01-01
We present a general method for including prior knowledge in a nonnegative matrix factorization (NMF), based on Gaussian process priors. We assume that the nonnegative factors in the NMF are linked by a strictly increasing function to an underlying Gaussian process specified by its covariance...... function. This allows us to find NMF decompositions that agree with our prior knowledge of the distribution of the factors, such as sparseness, smoothness, and symmetries. The method is demonstrated with an example from chemical shift brain imaging....
Nonnegative matrix factorization and its applications in pattern recognition
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Matrix factorization is an effective tool for large-scale data processing and analysis. Nonnegative matrix factorization (NMF) method, which decomposes the nonnegative matrix into two nonnegative factor matrices, provides a new way for matrix factorization. NMF is significant in intelligent information processing and pattern recognition. This paper firstly introduces the basic idea of NMF and some new relevant methods. Then we discuss the loss functions and relevant algorithms of NMF in the framework of probabilistic models based on our researches, and the relationship between NMF and information processing of perceptual process. Finally, we make use of NMF to deal with some practical questions of pattern recognition and point out some open problems for NMF.
Sparse Non-negative Matrix Factor 2-D Deconvolution
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N.
2006-01-01
We introduce the non-negative matrix factor 2-D deconvolution (NMF2D) model, which decomposes a matrix into a 2-dimensional convolution of two factor matrices. This model is an extension of the non-negative matrix factor deconvolution (NMFD) recently introduced by Smaragdis (2004). We derive...... and prove the convergence of two algorithms for NMF2D based on minimizing the squared error and the Kullback-Leibler divergence respectively. Next, we introduce a sparse non-negative matrix factor 2-D deconvolution model that gives easy interpretable decompositions and devise two algorithms for computing...... this form of factorization. The developed algorithms have been used for source separation and music transcription....
Learning Hidden Markov Models using Non-Negative Matrix Factorization
Cybenko, George
2008-01-01
The Baum-Welsh algorithm together with its derivatives and variations has been the main technique for learning Hidden Markov Models (HMM) from observational data. We present an HMM learning algorithm based on the non-negative matrix factorization (NMF) of higher order Markovian statistics that is structurally different from the Baum-Welsh and its associated approaches. The described algorithm supports estimation of the number of recurrent states of an HMM and iterates the non-negative matrix factorization (NMF) algorithm to improve the learned HMM parameters. Numerical examples are provided as well.
Efficient Nonnegative Matrix Factorization by DC Programming and DCA.
Le Thi, Hoai An; Vo, Xuan Thanh; Dinh, Tao Pham
2016-06-01
In this letter, we consider the nonnegative matrix factorization (NMF) problem and several NMF variants. Two approaches based on DC (difference of convex functions) programming and DCA (DC algorithm) are developed. The first approach follows the alternating framework that requires solving, at each iteration, two nonnegativity-constrained least squares subproblems for which DCA-based schemes are investigated. The convergence property of the proposed algorithm is carefully studied. We show that with suitable DC decompositions, our algorithm generates most of the standard methods for the NMF problem. The second approach directly applies DCA on the whole NMF problem. Two algorithms-one computing all variables and one deploying a variable selection strategy-are proposed. The proposed methods are then adapted to solve various NMF variants, including the nonnegative factorization, the smooth regularization NMF, the sparse regularization NMF, the multilayer NMF, the convex/convex-hull NMF, and the symmetric NMF. We also show that our algorithms include several existing methods for these NMF variants as special versions. The efficiency of the proposed approaches is empirically demonstrated on both real-world and synthetic data sets. It turns out that our algorithms compete favorably with five state-of-the-art alternating nonnegative least squares algorithms.
Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing
Gillis, Nicolas
2012-01-01
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representation. However, NMF has the drawback of being highly ill-posed, that is, there typically exist many different but equivalent factorizations. In this paper, we introduce a completely new way to obtaining more well-posed NMF problems whose solutions are sparser. Our technique is based on the preprocessing of the nonnegative input data matrix, and relies on the theory of M-matrices and the geometric interpretation of NMF. This approach provably leads to optimal and sparse solutions under the separability assumption of Donoho and Stodden (NIPS, 2003), and, for rank-three matrices, makes the number of exact factorizations finite. We illustrate the effectiveness of our technique on several image datasets.
Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization
Wang, Jim Jing-Yan
2012-01-01
Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.
Hierarchical subtask discovery with non-negative matrix factorization
CSIR Research Space (South Africa)
Earle, AC
2017-08-01
Full Text Available . Donoho, D. and Stodden, V. When does non-negative matrix factorization give a correct decomposition into parts? Proc. Advances in Neural Information Processing Systems 16, pp. 1141–1148, 2004. Hennequin, R., David, B., and Badeau, R. Beta-divergence as a... with Linearly Solvable Markov Decision Processes. arXiv, 2016. S¸ims¸ek, Ö. and Barto, A.S. Skill Characterization Based on Be- tweenness. Advances in Neural Information Processing Systems, pp. 1497–1504, 2009. Solway, A., Diuk, C., Córdova, N., Yee, D., Barto...
Graph Regularized Nonnegative Matrix Factorization for Hyperspectral Data Unmixing
Rajabi, Roozbeh; Ghassemian, Hassan
2011-01-01
Spectral unmixing is an important tool in hyperspectral data analysis for estimating endmembers and abundance fractions in a mixed pixel. This paper examines the applicability of a recently developed algorithm called graph regularized nonnegative matrix factorization (GNMF) for this aim. The proposed approach exploits the intrinsic geometrical structure of the data besides considering positivity and full additivity constraints. Simulated data based on the measured spectral signatures, is used for evaluating the proposed algorithm. Results in terms of abundance angle distance (AAD) and spectral angle distance (SAD) show that this method can effectively unmix hyperspectral data.
A flexible R package for nonnegative matrix factorization
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Seoighe Cathal
2010-07-01
Full Text Available Abstract Background Nonnegative Matrix Factorization (NMF is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community. Results Our objective is to provide the bioinformatics community with an open-source, easy-to-use and unified interface to standard NMF algorithms, as well as with a simple framework to help implement and test new NMF methods. For that purpose, we have developed a package for the R/BioConductor platform. The package ports public code to R, and is structured to enable users to easily modify and/or add algorithms. It includes a number of published NMF algorithms and initialization methods and facilitates the combination of these to produce new NMF strategies. Commonly used benchmark data and visualization methods are provided to help in the comparison and interpretation of the results. Conclusions The NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications. Documentation, source code and sample data are available from CRAN.
A fast algorithm for nonnegative matrix factorization and its convergence.
Li, Li-Xin; Wu, Lin; Zhang, Hui-Sheng; Wu, Fang-Xiang
2014-10-01
Nonnegative matrix factorization (NMF) has recently become a very popular unsupervised learning method because of its representational properties of factors and simple multiplicative update algorithms for solving the NMF. However, for the common NMF approach of minimizing the Euclidean distance between approximate and true values, the convergence of multiplicative update algorithms has not been well resolved. This paper first discusses the convergence of existing multiplicative update algorithms. We then propose a new multiplicative update algorithm for minimizing the Euclidean distance between approximate and true values. Based on the optimization principle and the auxiliary function method, we prove that our new algorithm not only converges to a stationary point, but also does faster than existing ones. To verify our theoretical results, the experiments on three data sets have been conducted by comparing our proposed algorithm with other existing methods.
Symmetric nonnegative matrix factorization: algorithms and applications to probabilistic clustering.
He, Zhaoshui; Xie, Shengli; Zdunek, Rafal; Zhou, Guoxu; Cichocki, Andrzej
2011-12-01
Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.
Sequences of Lower Bounds for the Perron Root of a Nonnegative Irreducible Matrix
Institute of Scientific and Technical Information of China (English)
ZHONG Qin; HUANG Ting Zhu
2009-01-01
Estimate bounds for the Perron root of a nonnegative matrix are important in theory of nonnegative matrices. It is more practical when the bounds are expressed as an easily calculated function in elements of matrices. For the Perron root of nonnegative irreducible matrices,three sequences of lower bounds are presented by means of constructing shifted matrices, whose convergence is studied. The comparisons of the sequences with known ones are supplemented with a numerical example.
Sparse Nonnegative Matrix Factorization Strategy for Cochlear Implants
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Hongmei Hu
2015-12-01
Full Text Available Current cochlear implant (CI strategies carry speech information via the waveform envelope in frequency subbands. CIs require efficient speech processing to maximize information transfer to the brain, especially in background noise, where the speech envelope is not robust to noise interference. In such conditions, the envelope, after decomposition into frequency bands, may be enhanced by sparse transformations, such as nonnegative matrix factorization (NMF. Here, a novel CI processing algorithm is described, which works by applying NMF to the envelope matrix (envelopogram of 22 frequency channels in order to improve performance in noisy environments. It is evaluated for speech in eight-talker babble noise. The critical sparsity constraint parameter was first tuned using objective measures and then evaluated with subjective speech perception experiments for both normal hearing and CI subjects. Results from vocoder simulations with 10 normal hearing subjects showed that the algorithm significantly enhances speech intelligibility with the selected sparsity constraints. Results from eight CI subjects showed no significant overall improvement compared with the standard advanced combination encoder algorithm, but a trend toward improvement of word identification of about 10 percentage points at +15 dB signal-to-noise ratio (SNR was observed in the eight CI subjects. Additionally, a considerable reduction of the spread of speech perception performance from 40% to 93% for advanced combination encoder to 80% to 100% for the suggested NMF coding strategy was observed.
Sparse Nonnegative Matrix Factorization Strategy for Cochlear Implants.
Hu, Hongmei; Lutman, Mark E; Ewert, Stephan D; Li, Guoping; Bleeck, Stefan
2015-12-30
Current cochlear implant (CI) strategies carry speech information via the waveform envelope in frequency subbands. CIs require efficient speech processing to maximize information transfer to the brain, especially in background noise, where the speech envelope is not robust to noise interference. In such conditions, the envelope, after decomposition into frequency bands, may be enhanced by sparse transformations, such as nonnegative matrix factorization (NMF). Here, a novel CI processing algorithm is described, which works by applying NMF to the envelope matrix (envelopogram) of 22 frequency channels in order to improve performance in noisy environments. It is evaluated for speech in eight-talker babble noise. The critical sparsity constraint parameter was first tuned using objective measures and then evaluated with subjective speech perception experiments for both normal hearing and CI subjects. Results from vocoder simulations with 10 normal hearing subjects showed that the algorithm significantly enhances speech intelligibility with the selected sparsity constraints. Results from eight CI subjects showed no significant overall improvement compared with the standard advanced combination encoder algorithm, but a trend toward improvement of word identification of about 10 percentage points at +15 dB signal-to-noise ratio (SNR) was observed in the eight CI subjects. Additionally, a considerable reduction of the spread of speech perception performance from 40% to 93% for advanced combination encoder to 80% to 100% for the suggested NMF coding strategy was observed.
COMPUTING A NEAREST P-SYMMETRIC NONNEGATIVE DEFINITE MATRIX UNDER LINEAR RESTRICTION
Institute of Scientific and Technical Information of China (English)
Hua Dai
2004-01-01
Let P be an n × n symmetric orthogonal matrix. A real n × n matrix A is called P-symmetric nonnegative definite if A is symmetric nonnegative definite and (PA)T =PA. This paper is concerned with a kind of inverse problem for P-symmetric nonncgative definite matrices: Given a real n × n matrix A, real n × m matrices X and B, find an n × n P-symmetric nonnegative definite matrix A minimizing ‖A- A‖F subject to AX = B.Necessary and sufficient conditions are presented for the solvability of the problem. The expression of the solution to the problem is given. These results are applied to solve an inverse eigenvalue problem for P-symmetric nonnegative definite matrices.
Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks
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Enming Dong
2014-01-01
Full Text Available Low rank matrices approximations have been used in link prediction for networks, which are usually global optimal methods and lack of using the local information. The block structure is a significant local feature of matrices: entities in the same block have similar values, which implies that links are more likely to be found within dense blocks. We use this insight to give a probabilistic latent variable model for finding missing links by convex nonnegative matrix factorization with block detection. The experiments show that this method gives better prediction accuracy than original method alone. Different from the original low rank matrices approximations methods for link prediction, the sparseness of solutions is in accord with the sparse property for most real complex networks. Scaling to massive size network, we use the block information mapping matrices onto distributed architectures and give a divide-and-conquer prediction method. The experiments show that it gives better results than common neighbors method when the networks have a large number of missing links.
Multiplicative algorithms for constrained non-negative matrix factorization
Peng, Chengbin
2012-12-01
Non-negative matrix factorization (NMF) provides the advantage of parts-based data representation through additive only combinations. It has been widely adopted in areas like item recommending, text mining, data clustering, speech denoising, etc. In this paper, we provide an algorithm that allows the factorization to have linear or approximatly linear constraints with respect to each factor. We prove that if the constraint function is linear, algorithms within our multiplicative framework will converge. This theory supports a large variety of equality and inequality constraints, and can facilitate application of NMF to a much larger domain. Taking the recommender system as an example, we demonstrate how a specialized weighted and constrained NMF algorithm can be developed to fit exactly for the problem, and the tests justify that our constraints improve the performance for both weighted and unweighted NMF algorithms under several different metrics. In particular, on the Movielens data with 94% of items, the Constrained NMF improves recall rate 3% compared to SVD50 and 45% compared to SVD150, which were reported as the best two in the top-N metric. © 2012 IEEE.
A Multidimensional Nonnegative Matrix Factorization Model for Retweeting Behavior Prediction
Directory of Open Access Journals (Sweden)
Mengmeng Wang
2015-01-01
Full Text Available Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. As a consequence, exploring on retweeting behavior is a better way to understand microblog’s transmissibility in the network. Hence, targeted at online microblogging, a directed social network, along with user-based features, this paper first built content-based features, which consisted of URL, hashtag, emotion difference, and interest similarity, based on time series of text information that user posts. And then we measure relationship-based factor in social network according to frequency of interactions and network structure which blend with temporal information. Finally, we utilize nonnegative matrix factorization to predict user’s retweeting behavior from user-based dimension and content-based dimension, respectively, by employing strength of social relationship to constrain objective function. The results suggest that our proposed method effectively increases retweeting behavior prediction accuracy and provides a new train of thought for retweeting behavior prediction in dynamic social networks.
Approximate L0 constrained Non-negative Matrix and Tensor Factorization
DEFF Research Database (Denmark)
Mørup, Morten; Madsen, Kristoffer Hougaard; Hansen, Lars Kai
2008-01-01
Non-negative matrix factorization (NMF), i.e. V = WH where both V, W and H are non-negative has become a widely used blind source separation technique due to its part based representation. The NMF decomposition is not in general unique and a part based representation not guaranteed. However...... path for the L1 norm regularized least squares NMF for fixed W can be calculated at the cost of an ordinary least squares solution based on a modification of the Least Angle Regression and Selection (LARS) algorithm forming a non-negativity constrained LARS (NLARS). With the full regularization path...
Slow features nonnegative matrix factorization for temporal data decomposition
Zafeiriou, Lazaros; Nikitidis, Symeon; Zafeiriou, Stefanos; Pantic, Maja
2014-01-01
In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning into a single framework that aims to learn slow varying parts-based representations of time varying sequences. We demonstrate that the proposed algorithm arises naturally by embedding the Slow Features
Non-negative matrix factorization and term structure of interest rates
Takada, Hellinton H.; Stern, Julio M.
2015-01-01
Non-Negative Matrix Factorization (NNMF) is a technique for dimensionality reduction with a wide variety of applications from text mining to identification of concentrations in chemistry. NNMF deals with non-negative data and results in non-negative factors and factor loadings. Consequently, it is a natural choice when studying the term structure of interest rates. In this paper, NNMF is applied to obtain factors from the term structure of interest rates and the procedure is compared with other very popular techniques: principal component analysis and Nelson-Siegel model. The NNMF approximation for the term structure of interest rates is better in terms of fitting. From a practitioner point of view, the NNMF factors and factor loadings obtained possess straightforward financial interpretations due to their non-negativeness.
Ma, Yuanyuan; Hu, Xiaohua; He, Tingting; Jiang, Xingpeng
2017-09-26
Many datasets existed in the real world are often comprised of different representations or views which provide complementary information to each other. To integrate information from multiple views, data integration approaches such as nonnegative matrix factorization (NMF) have been developed to combine multiple heterogeneous data simultaneously to obtain a comprehensive representation. In this paper, we proposed a novel variant of symmetric nonnegative matrix factorization (SNMF), called Laplacian regularization based joint symmetric nonnegative matrix factorization (LJ-SNMF) for clustering multi-view data. We conduct extensive experiments on several realistic datasets including Human Microbiome Project data. The experimental results show that the proposed method outperforms other variants of NMF, which suggests the potential application of LJ-SNMF in clustering multi-view datasets. Additionally, we also demonstrate the capability of LJ-SNMF in community finding.
Directory of Open Access Journals (Sweden)
Shota Saito
Full Text Available Micro-blogging services, such as Twitter, offer opportunities to analyse user behaviour. Discovering and distinguishing behavioural patterns in micro-blogging services is valuable. However, it is difficult and challenging to distinguish users, and to track the temporal development of collective attention within distinct user groups in Twitter. In this paper, we formulate this problem as tracking matrices decomposed by Nonnegative Matrix Factorisation for time-sequential matrix data, and propose a novel extension of Nonnegative Matrix Factorisation, which we refer to as Time Evolving Nonnegative Matrix Factorisation (TENMF. In our method, we describe users and words posted in some time interval by a matrix, and use several matrices as time-sequential data. Subsequently, we apply Time Evolving Nonnegative Matrix Factorisation to these time-sequential matrices. TENMF can decompose time-sequential matrices, and can track the connection among decomposed matrices, whereas previous NMF decomposes a matrix into two lower dimension matrices arbitrarily, which might lose the time-sequential connection. Our proposed method has an adequately good performance on artificial data. Moreover, we present several results and insights from experiments using real data from Twitter.
Saito, Shota; Hirata, Yoshito; Sasahara, Kazutoshi; Suzuki, Hideyuki
2015-01-01
Micro-blogging services, such as Twitter, offer opportunities to analyse user behaviour. Discovering and distinguishing behavioural patterns in micro-blogging services is valuable. However, it is difficult and challenging to distinguish users, and to track the temporal development of collective attention within distinct user groups in Twitter. In this paper, we formulate this problem as tracking matrices decomposed by Nonnegative Matrix Factorisation for time-sequential matrix data, and propose a novel extension of Nonnegative Matrix Factorisation, which we refer to as Time Evolving Nonnegative Matrix Factorisation (TENMF). In our method, we describe users and words posted in some time interval by a matrix, and use several matrices as time-sequential data. Subsequently, we apply Time Evolving Nonnegative Matrix Factorisation to these time-sequential matrices. TENMF can decompose time-sequential matrices, and can track the connection among decomposed matrices, whereas previous NMF decomposes a matrix into two lower dimension matrices arbitrarily, which might lose the time-sequential connection. Our proposed method has an adequately good performance on artificial data. Moreover, we present several results and insights from experiments using real data from Twitter.
Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent
Directory of Open Access Journals (Sweden)
Zunyi Tang
2013-01-01
Full Text Available Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.
Bayesian Nonnegative Matrix Factorization with Volume Prior for Unmixing of Hyperspectral Images
DEFF Research Database (Denmark)
Arngren, Morten; Schmidt, Mikkel Nørgaard; Larsen, Jan
2009-01-01
In hyperspectral image analysis the objective is to unmix a set of acquired pixels into pure spectral signatures (endmembers) and corresponding fractional abundances. The Non-negative Matrix Factorization (NMF) methods have received a lot of attention for this unmixing process. Many of these NMF...
Non-negative matrix analysis in x-ray spectromicroscopy: choosing regularizers
Mak, Rachel; Wild, Stefan M.; Jacobsen, Chris
2016-01-01
In x-ray spectromicroscopy, a set of images can be acquired across an absorption edge to reveal chemical speciation. We previously described the use of non-negative matrix approximation methods for improved classification and analysis of these types of data. We present here an approach to find appropriate values of regularization parameters for this optimization approach. PMID:27041779
Semi-Supervised Half-Quadratic Nonnegative Matrix Factorization for Face Recognition
Alghamdi, Masheal M.
2014-05-01
Face recognition is a challenging problem in computer vision. Difficulties such as slight differences between similar faces of different people, changes in facial expressions, light and illumination condition, and pose variations add extra complications to the face recognition research. Many algorithms are devoted to solving the face recognition problem, among which the family of nonnegative matrix factorization (NMF) algorithms has been widely used as a compact data representation method. Different versions of NMF have been proposed. Wang et al. proposed the graph-based semi-supervised nonnegative learning (S2N2L) algorithm that uses labeled data in constructing intrinsic and penalty graph to enforce separability of labeled data, which leads to a greater discriminating power. Moreover the geometrical structure of labeled and unlabeled data is preserved through using the smoothness assumption by creating a similarity graph that conserves the neighboring information for all labeled and unlabeled data. However, S2N2L is sensitive to light changes, illumination, and partial occlusion. In this thesis, we propose a Semi-Supervised Half-Quadratic NMF (SSHQNMF) algorithm that combines the benefits of S2N2L and the robust NMF by the half- quadratic minimization (HQNMF) algorithm.Our algorithm improves upon the S2N2L algorithm by replacing the Frobenius norm with a robust M-Estimator loss function. A multiplicative update solution for our SSHQNMF algorithmis driven using the half- 4 quadratic (HQ) theory. Extensive experiments on ORL, Yale-A and a subset of the PIE data sets for nine M-estimator loss functions for both SSHQNMF and HQNMF algorithms are investigated, and compared with several state-of-the-art supervised and unsupervised algorithms, along with the original S2N2L algorithm in the context of classification, clustering, and robustness against partial occlusion. The proposed algorithm outperformed the other algorithms. Furthermore, SSHQNMF with Maximum Correntropy
Functional biogeography of ocean microbes revealed through non-negative matrix factorization.
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Xingpeng Jiang
Full Text Available The direct "metagenomic" sequencing of genomic material from complex assemblages of bacteria, archaea, viruses and microeukaryotes has yielded new insights into the structure of microbial communities. For example, analysis of metagenomic data has revealed the existence of previously unknown microbial taxa whose spatial distributions are limited by environmental conditions, ecological competition, and dispersal mechanisms. However, differences in genotypes that might lead biologists to designate two microbes as taxonomically distinct need not necessarily imply differences in ecological function. Hence, there is a growing need for large-scale analysis of the distribution of microbial function across habitats. Here, we present a framework for investigating the biogeography of microbial function by analyzing the distribution of protein families inferred from environmental sequence data across a global collection of sites. We map over 6,000,000 protein sequences from unassembled reads from the Global Ocean Survey dataset to [Formula: see text] protein families, generating a protein family relative abundance matrix that describes the distribution of each protein family across sites. We then use non-negative matrix factorization (NMF to approximate these protein family profiles as linear combinations of a small number of ecological components. Each component has a characteristic functional profile and site profile. Our approach identifies common functional signatures within several of the components. We use our method as a filter to estimate functional distance between sites, and find that an NMF-filtered measure of functional distance is more strongly correlated with environmental distance than a comparable PCA-filtered measure. We also find that functional distance is more strongly correlated with environmental distance than with geographic distance, in agreement with prior studies. We identify similar protein functions in several components and
Naik, Ganesh R; Nguyen, Hung T
2015-03-01
Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis.
Nonnegative Matrix Factor 2-D Deconvolution for Blind Single Channel Source Separation
DEFF Research Database (Denmark)
Schmidt, Mikkel N.; Mørup, Morten
2006-01-01
We present a novel method for blind separation of instruments in polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to in...... to individual instruments. Based on this factorization we separate the instruments using spectrogram masking. The proposed algorithm has applications in computational auditory scene analysis, music information retrieval, and automatic music transcription.......We present a novel method for blind separation of instruments in polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding...
Single-channel source separation using non-negative matrix factorization
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard
, in which a number of methods for single-channel source separation based on non-negative matrix factorization are presented. In the papers, the methods are applied to separating audio signals such as speech and musical instruments and separating different types of tissue in chemical shift imaging.......Single-channel source separation problems occur when a number of sources emit signals that are mixed and recorded by a single sensor, and we are interested in estimating the original source signals based on the recorded mixture. This problem, which occurs in many sciences, is inherently under......-determined and its solution relies on making appropriate assumptions concerning the sources. This dissertation is concerned with model-based probabilistic single-channel source separation based on non-negative matrix factorization, and consists of two parts: i) three introductory chapters and ii) five published...
Real-time detection of overlapping sound events with non-negative matrix factorization
Dessein, Arnaud; Cont, Arshia; Lemaitre, Guillaume
2013-01-01
International audience; In this paper, we investigate the problem of real-time detection of overlapping sound events by employing non-negative matrix factorization techniques. We consider a setup where audio streams arrive in real-time to the system and are decomposed onto a dictionary of event templates learned off-line prior to the decomposition. An important drawback of existing approaches in this context is the lack of controls on the decomposition. We propose and compare two provably con...
Supervised non-negative matrix factorization based latent semantic image indexing
Institute of Scientific and Technical Information of China (English)
Dong Liang; Jie Yang; Yuchou Chang
2006-01-01
@@ A novel latent semantic indexing (LSI) approach for content-based image retrieval is presented in this paper. Firstly, an extension of non-negative matrix factorization (NMF) to supervised initialization isdiscussed. Then, supervised NMF is used in LSI to find the relationships between low-level features and high-level semantics. The retrieved results are compared with other approaches and a good performance is obtained.
A Sharp upper bound for the spectral radius of a nonnegative matrix and applications
You, Lihua; Shu, Yujie; Zhang, Xiao-Dong
2016-01-01
In this paper, we obtain a sharp upper bound for the spectral radius of a nonnegative matrix. This result is used to present upper bounds for the adjacency spectral radius, the Laplacian spectral radius, the signless Laplacian spectral radius, the distance spectral radius, the distance Laplacian spectral radius, the distance signless Laplacian spectral radius of a graph or a digraph. These results are new or generalize some known results.
Nonnegative Matrix Factorization Numerical Method for Integrated Photonic Cavity Based Spectroscopy
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Zhengyu Huang
2014-01-01
Full Text Available Nonnegative matrix factorization numerical method has been used to improve the spectral resolution of integrated photonic cavity based spectroscopy. Based on the experimental results for integrated photonic cavity device on Optics Letters 32, 632 (2007, the theoretical results show that the spectral resolution can be improved more than 3 times from 5.5 nm to 1.8 nm. It is a promising way to release the difficulty of fabricating high-resolution devices.
Gillis, Nicolas
2011-01-01
Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of applications such as text mining, image processing, hyperspectral data analysis, computational biology, and clustering. In this paper, we consider two well-known algorithms designed to solve NMF problems, namely the multiplicative updates of Lee and Seung and the hierarchical alternating least squares of Cichocki et al. We propose a simple way to significantly accelerate their convergence, based on a careful analysis of the computational cost needed at each iteration. This acceleration technique can also be applied to other algorithms, which we illustrate on the projected gradient method of Lin. The efficiency of the accelerated algorithms is empirically demonstrated on image and text datasets, and compares favorably with a state-of-the-art alternating nonnegative least squares algorithm. Finally, we provide a theoretical argument based on the properties of NMF and its solutions that explains in particular the very ...
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation
DEFF Research Database (Denmark)
Brouwer, Thomas; Frellsen, Jes; Liò, Pietro
We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation. We show that our approach achieves faster convergence per iteration and timestep (wall-clock) than Gibbs sampling and non-probabilistic approaches, and do not require additional...... samples to estimate the posterior. We show that in particular for matrix tri-factorisation convergence is difficult, but our variational Bayesian approach offers a fast solution, allowing the tri-factorisation approach to be used more effectively....
Non-negative matrix factorization by maximizing correntropy for cancer clustering
Wang, Jim Jing-Yan
2013-03-24
Background: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize either the l2 norm or the Kullback-Leibler distance between the product of the two matrices and the original matrix. Correntropy was recently shown to be an effective similarity measurement due to its stability to outliers or noise.Results: We propose a maximum correntropy criterion (MCC)-based NMF method (NMF-MCC) for gene expression data-based cancer clustering. Instead of minimizing the l2 norm or the Kullback-Leibler distance, NMF-MCC maximizes the correntropy between the product of the two matrices and the original matrix. The optimization problem can be solved by an expectation conditional maximization algorithm.Conclusions: Extensive experiments on six cancer benchmark sets demonstrate that the proposed method is significantly more accurate than the state-of-the-art methods in cancer clustering. 2013 Wang et al.; licensee BioMed Central Ltd.
Institute of Scientific and Technical Information of China (English)
Zheng Zhonglong; Yang Jie
2005-01-01
Many problems in image representation and classification involve some form of dimensionality reduction. Non-negative matrix factorization (NMF) is a recently proposed unsupervised procedure for learning spatially localized, parts-based subspace representation of objects. An improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability is presented. The new method with principal component analysis (PCA) and locally linear embedding (LLE) proposed recently in Science are compared. Finally, the new method to several real world datasets and achieve good performance in representation and classification is applied.
On the Equivalence of Nonnegative Matrix Factorization and K-means- Spectral Clustering
Energy Technology Data Exchange (ETDEWEB)
Ding, Chris; He, Xiaofeng; Simon, Horst D.; Jin, Rong
2005-12-04
We provide a systematic analysis of nonnegative matrix factorization (NMF) relating to data clustering. We generalize the usual X = FG{sup T} decomposition to the symmetric W = HH{sup T} and W = HSH{sup T} decompositions. We show that (1) W = HH{sup T} is equivalent to Kernel K-means clustering and the Laplacian-based spectral clustering. (2) X = FG{sup T} is equivalent to simultaneous clustering of rows and columns of a bipartite graph. We emphasizes the importance of orthogonality in NMF and soft clustering nature of NMF. These results are verified with experiments on face images and newsgroups.
Single-Channel Speech Separation using Sparse Non-Negative Matrix Factorization
DEFF Research Database (Denmark)
Schmidt, Mikkel N.; Olsson, Rasmus Kongsgaard
2007-01-01
We apply machine learning techniques to the problem of separating multiple speech sources from a single microphone recording. The method of choice is a sparse non-negative matrix factorization algorithm, which in an unsupervised manner can learn sparse representations of the data. This is applied...... to the learning of personalized dictionaries from a speech corpus, which in turn are used to separate the audio stream into its components. We show that computational savings can be achieved by segmenting the training data on a phoneme level. To split the data, a conventional speech recognizer is used...
A novel edge-preserving nonnegative matrix factorization method for spectral unmixing
Bao, Wenxing; Ma, Ruishi
2015-12-01
Spectral unmixing technique is one of the key techniques to identify and classify the material in the hyperspectral image processing. A novel robust spectral unmixing method based on nonnegative matrix factorization(NMF) is presented in this paper. This paper used an edge-preserving function as hypersurface cost function to minimize the nonnegative matrix factorization. To minimize the hypersurface cost function, we constructed the updating functions for signature matrix of end-members and abundance fraction respectively. The two functions are updated alternatively. For evaluation purpose, synthetic data and real data have been used in this paper. Synthetic data is used based on end-members from USGS digital spectral library. AVIRIS Cuprite dataset have been used as real data. The spectral angle distance (SAD) and abundance angle distance(AAD) have been used in this research for assessment the performance of proposed method. The experimental results show that this method can obtain more ideal results and good accuracy for spectral unmixing than present methods.
Chang, Hsuan T; Shui, J-W; Lin, K-P
2017-02-01
In this paper, a joint multiple-image encryption and multiplexing system, which utilizes both the nonnegative matrix factorization (NMF) scheme and digital holography, is proposed. A number of images are transformed into noise-like digital holograms, which are then decomposed into a defined number of basis images and a corresponding weighting matrix using the NMF scheme. The determined basis images are similar to the digital holograms and appear as noise-like patterns, which are then stored as encrypted data and serve as the lock in an encryption system. On the other hand, the column vectors in the weighting matrix serve as the keys for the corresponding plain images or the addresses of the multiplexed images. Both the increased uniformity of the column weighting factors and the parameters used in the digital holography enhance the security of the distributed keys. The experimental results show that the proposed method can successfully perform multiple-image encryption with high-level security.
Yang, Haixuan; Seoighe, Cathal
2016-01-01
Nonnegative Matrix Factorization (NMF) has proved to be an effective method for unsupervised clustering analysis of gene expression data. By the nonnegativity constraint, NMF provides a decomposition of the data matrix into two matrices that have been used for clustering analysis. However, the decomposition is not unique. This allows different clustering results to be obtained, resulting in different interpretations of the decomposition. To alleviate this problem, some existing methods directly enforce uniqueness to some extent by adding regularization terms in the NMF objective function. Alternatively, various normalization methods have been applied to the factor matrices; however, the effects of the choice of normalization have not been carefully investigated. Here we investigate the performance of NMF for the task of cancer class discovery, under a wide range of normalization choices. After extensive evaluations, we observe that the maximum norm showed the best performance, although the maximum norm has not previously been used for NMF. Matlab codes are freely available from: http://maths.nuigalway.ie/~haixuanyang/pNMF/pNMF.htm. PMID:27741311
Ma, Yehao; Li, Xian; Huang, Pingjie; Hou, Dibo; Wang, Qiang; Zhang, Guangxin
2017-04-01
In many situations the THz spectroscopic data observed from complex samples represent the integrated result of several interrelated variables or feature components acting together. The actual information contained in the original data might be overlapping and there is a necessity to investigate various approaches for model reduction and data unmixing. The development and use of low-rank approximate nonnegative matrix factorization (NMF) and smooth constraint NMF (CNMF) algorithms for feature components extraction and identification in the fields of terahertz time domain spectroscopy (THz-TDS) data analysis are presented. The evolution and convergence properties of NMF and CNMF methods based on sparseness, independence and smoothness constraints for the resulting nonnegative matrix factors are discussed. For general NMF, its cost function is nonconvex and the result is usually susceptible to initialization and noise corruption, and may fall into local minima and lead to unstable decomposition. To reduce these drawbacks, smoothness constraint is introduced to enhance the performance of NMF. The proposed algorithms are evaluated by several THz-TDS data decomposition experiments including a binary system and a ternary system simulating some applications such as medicine tablet inspection. Results show that CNMF is more capable of finding optimal solutions and more robust for random initialization in contrast to NMF. The investigated method is promising for THz data resolution contributing to unknown mixture identification.
Zhou, Guoxu; Yang, Zuyuan; Xie, Shengli; Yang, Jun-Mei
2011-04-01
Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.
Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation
Benzi, Kirell; Bresson, Xavier; Vandergheynst, Pierre
2016-01-01
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using a rich combination of audio, meta-data and social features. As we demonstrate, our hybrid recommendation system is very versatile and incorporates several well-known methods while outperforming them. Particularly, we show on real-world data that our model overcomes w.r.t. two evaluation metrics the recommendation of models solely based on low-rank information, graph-based information or a combination of both.
Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methods
Ozer, Mert; Davulcu, Hasan
2016-01-01
Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information in community detection. We show that user content and endorsement filtered connectivity information are complementary to each other in clustering politically motivated users into pure political communities. Word usage is the strongest indicator of users' political orientation among all content categories. Incorporating user-word matrix and word similarity regularizer provides the missing link in connectivity only methods which suffer from detection of artificially large number of clusters for Twitter networks.
Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification.
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Xiang Zhang
Full Text Available Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.
Detecting cells using non-negative matrix factorization on calcium imaging data.
Maruyama, Ryuichi; Maeda, Kazuma; Moroda, Hajime; Kato, Ichiro; Inoue, Masashi; Miyakawa, Hiroyoshi; Aonishi, Toru
2014-07-01
We propose a cell detection algorithm using non-negative matrix factorization (NMF) on Ca2+ imaging data. To apply NMF to Ca2+ imaging data, we use the bleaching line of the background fluorescence intensity as an a priori background constraint to make the NMF uniquely dissociate the background component from the image data. This constraint helps us to incorporate the effect of dye-bleaching and reduce the non-uniqueness of the solution. We demonstrate that in the case of noisy data, the NMF algorithm can detect cells more accurately than Mukamel's independent component analysis algorithm, a state-of-art method. We then apply the NMF algorithm to Ca2+ imaging data recorded on the local activities of subcellular structures of multiple cells in a wide area. We show that our method can decompose rapid transient components corresponding to somas and dendrites of many neurons, and furthermore, that it can decompose slow transient components probably corresponding to glial cells.
Cao, Xiaochun; Wang, Xiao; Jin, Di; Cao, Yixin; He, Dongxiao
2013-10-21
Community detection is important for understanding networks. Previous studies observed that communities are not necessarily disjoint and might overlap. It is also agreed that some outlier vertices participate in no community, and some hubs in a community might take more important roles than others. Each of these facts has been independently addressed in previous work. But there is no algorithm, to our knowledge, that can identify these three structures altogether. To overcome this limitation, we propose a novel model where vertices are measured by their centrality in communities, and define the identification of overlapping communities, hubs, and outliers as an optimization problem, calculated by nonnegative matrix factorization. We test this method on various real networks, and compare it with several competing algorithms. The experimental results not only demonstrate its ability of identifying overlapping communities, hubs, and outliers, but also validate its superior performance in terms of clustering quality.
Deep learning and non-negative matrix factorization in recognition of mammograms
Swiderski, Bartosz; Kurek, Jaroslaw; Osowski, Stanislaw; Kruk, Michal; Barhoumi, Walid
2017-02-01
This paper presents novel approach to the recognition of mammograms. The analyzed mammograms represent the normal and breast cancer (benign and malignant) cases. The solution applies the deep learning technique in image recognition. To obtain increased accuracy of classification the nonnegative matrix factorization and statistical self-similarity of images are applied. The images reconstructed by using these two approaches enrich the data base and thanks to this improve of quality measures of mammogram recognition (increase of accuracy, sensitivity and specificity). The results of numerical experiments performed on large DDSM data base containing more than 10000 mammograms have confirmed good accuracy of class recognition, exceeding the best results reported in the actual publications for this data base.
Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization
Energy Technology Data Exchange (ETDEWEB)
Chennubhotla, Chakra [University of Pittsburgh School of Medicine, Pittsburgh PA; Castro, Jason [Bates College
2013-01-01
In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain un- clear. Here, we use non-negative matrix factorization (NMF) - a dimensionality reduction technique - to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor di- mensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner. We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures.
Park, Sang Ha; Lee, Seokjin; Sung, Koeng-Mo
Non-negative matrix factorization (NMF) is widely used for monaural musical sound source separation because of its efficiency and good performance. However, an additional clustering process is required because the musical sound mixture is separated into more signals than the number of musical tracks during NMF separation. In the conventional method, manual clustering or training-based clustering is performed with an additional learning process. Recently, a clustering algorithm based on the mel-frequency cepstrum coefficient (MFCC) was proposed for unsupervised clustering. However, MFCC clustering supplies limited information for clustering. In this paper, we propose various timbre features for unsupervised clustering and a clustering algorithm with these features. Simulation experiments are carried out using various musical sound mixtures. The results indicate that the proposed method improves clustering performance, as compared to conventional MFCC-based clustering.
Montcuquet, Anne-Sophie; Hervé, Lionel; Navarro, Fabrice; Dinten, Jean-Marc; Mars, Jérôme I
2011-09-01
Fluorescence imaging locates fluorescent markers that specifically bind to targets; like tumors, markers are injected to a patient, optimally excited with near-infrared light, and located thanks to backward-emitted fluorescence analysis. To investigate thick and diffusive media, as the fluorescence signal decreases exponentially with the light travel distance, the autofluorescence of biological tissues comes to be a limiting factor. To remove autofluorescence and isolate specific fluorescence, a spectroscopic approach, based on nonnegative matrix factorization (NMF), is explored. To improve results on spatially sparse markers detection, we suggest a new constrained NMF algorithm that takes sparsity constraints into account. A comparative study between both algorithms is proposed on simulated and in vivo data.
Montcuquet, Anne-Sophie; Hervé, Lionel; Navarro, Fabrice; Dinten, Jean-Marc; Mars, Jérôme I
2010-01-01
Fluorescence imaging in diffusive media is an emerging imaging modality for medical applications that uses injected fluorescent markers that bind to specific targets, e.g., carcinoma. The region of interest is illuminated with near-IR light and the emitted back fluorescence is analyzed to localize the fluorescence sources. To investigate a thick medium, as the fluorescence signal decreases with the light travel distance, any disturbing signal, such as biological tissues intrinsic fluorescence (called autofluorescence) is a limiting factor. Several specific markers may also be simultaneously injected to bind to different molecules, and one may want to isolate each specific fluorescent signal from the others. To remove the unwanted fluorescence contributions or separate different specific markers, a spectroscopic approach is explored. The nonnegative matrix factorization (NMF) is the blind positive source separation method we chose. We run an original regularized NMF algorithm we developed on experimental data, and successfully obtain separated in vivo fluorescence spectra.
Directory of Open Access Journals (Sweden)
Qunyi Xie
2016-01-01
Full Text Available Content-based image retrieval has recently become an important research topic and has been widely used for managing images from repertories. In this article, we address an efficient technique, called MNGS, which integrates multiview constrained nonnegative matrix factorization (NMF and Gaussian mixture model- (GMM- based spectral clustering for image retrieval. In the proposed methodology, the multiview NMF scheme provides competitive sparse representations of underlying images through decomposition of a similarity-preserving matrix that is formed by fusing multiple features from different visual aspects. In particular, the proposed method merges manifold constraints into the standard NMF objective function to impose an orthogonality constraint on the basis matrix and satisfy the structure preservation requirement of the coefficient matrix. To manipulate the clustering method on sparse representations, this paper has developed a GMM-based spectral clustering method in which the Gaussian components are regrouped in spectral space, which significantly improves the retrieval effectiveness. In this way, image retrieval of the whole database translates to a nearest-neighbour search in the cluster containing the query image. Simultaneously, this study investigates the proof of convergence of the objective function and the analysis of the computational complexity. Experimental results on three standard image datasets reveal the advantages that can be achieved with the proposed retrieval scheme.
A perturbation-based framework for link prediction via non-negative matrix factorization
Wang, Wenjun; Cai, Fei; Jiao, Pengfei; Pan, Lin
2016-12-01
Many link prediction methods have been developed to infer unobserved links or predict latent links based on the observed network structure. However, due to network noises and irregular links in real network, the performances of existed methods are usually limited. Considering random noises and irregular links, we propose a perturbation-based framework based on Non-negative Matrix Factorization to predict missing links. We first automatically determine the suitable number of latent features, which is inner rank in NMF, by Colibri method. Then, we perturb training set of a network by perturbation sets many times and get a series of perturbed networks. Finally, the common basis matrix and coefficients matrix of these perturbed networks are obtained via NMF and form similarity matrix of the network for link prediction. Experimental results on fifteen real networks show that the proposed framework has competitive performances compared with state-of-the-art link prediction methods. Correlations between the performances of different methods and the statistics of networks show that those methods with good precisions have similar consistence.
Devarajan, Karthik; Cheung, Vincent C.K.
2017-01-01
Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two nonnegative matrices, W and H where V ~ WH. It has been successfully applied in the analysis and interpretation of large-scale data arising in neuroscience, computational biology and natural language processing, among other areas. A distinctive feature of NMF is its nonnegativity constraints that allow only additive linear combinations of the data, thus enabling it to learn parts that have distinct physical representations in reality. In this paper, we describe an information-theoretic approach to NMF for signal-dependent noise based on the generalized inverse Gaussian model. Specifically, we propose three novel algorithms in this setting, each based on multiplicative updates and prove monotonicity of updates using the EM algorithm. In addition, we develop algorithm-specific measures to evaluate their goodness-of-fit on data. Our methods are demonstrated using experimental data from electromyography studies as well as simulated data in the extraction of muscle synergies, and compared with existing algorithms for signal-dependent noise. PMID:24684448
Robust and Non-Negative Collective Matrix Factorization for Text-to-Image Transfer Learning.
Yang, Liu; Jing, Liping; Ng, Michael K
2015-12-01
Heterogeneous transfer learning has recently gained much attention as a new machine learning paradigm in which the knowledge can be transferred from source domains to target domains in different feature spaces. Existing works usually assume that source domains can provide accurate and useful knowledge to be transferred to target domains for learning. In practice, there may be noise appearing in given source (text) and target (image) domains data, and thus, the performance of transfer learning can be seriously degraded. In this paper, we propose a robust and non-negative collective matrix factorization model to handle noise in text-to-image transfer learning, and make a reliable bridge to transfer accurate and useful knowledge from the text domain to the image domain. The proposed matrix factorization model can be solved by an efficient iterative method, and the convergence of the iterative method can be shown. Extensive experiments on real data sets suggest that the proposed model is able to effectively perform transfer learning in noisy text and image domains, and it is superior to the popular existing methods for text-to-image transfer learning.
Exploring Mixed Membership Stochastic Block Models via Non-negative Matrix Factorization
Peng, Chengbin
2014-12-01
Many real-world phenomena can be modeled by networks in which entities and connections are represented by nodes and edges respectively. When certain nodes are highly connected with each other, those nodes forms a cluster, which is called community in our context. It is usually assumed that each node belongs to one community only, but evidences in biology and social networks reveal that the communities often overlap with each other. In other words, one node can probably belong to multiple communities. In light of that, mixed membership stochastic block models (MMB) have been developed to model those networks with overlapping communities. Such a model contains three matrices: two incidence matrices indicating in and out connections and one probability matrix. When the probability of connections for nodes between communities are significantly small, the parameter inference problem to this model can be solved by a constrained non-negative matrix factorization (NMF) algorithm. In this paper, we explore the connection between the two models and propose an algorithm based on NMF to infer the parameters of MMB. The proposed algorithms can detect overlapping communities regardless of knowing or not the number of communities. Experiments show that our algorithm can achieve a better community detection performance than the traditional NMF algorithm. © 2014 IEEE.
Scalable Linear Visual Feature Learning via Online Parallel Nonnegative Matrix Factorization.
Zhao, Xueyi; Li, Xi; Zhang, Zhongfei; Shen, Chunhua; Zhuang, Yueting; Gao, Lixin; Li, Xuelong
2016-12-01
Visual feature learning, which aims to construct an effective feature representation for visual data, has a wide range of applications in computer vision. It is often posed as a problem of nonnegative matrix factorization (NMF), which constructs a linear representation for the data. Although NMF is typically parallelized for efficiency, traditional parallelization methods suffer from either an expensive computation or a high runtime memory usage. To alleviate this problem, we propose a parallel NMF method called alternating least square block decomposition (ALSD), which efficiently solves a set of conditionally independent optimization subproblems based on a highly parallelized fine-grained grid-based blockwise matrix decomposition. By assigning each block optimization subproblem to an individual computing node, ALSD can be effectively implemented in a MapReduce-based Hadoop framework. In order to cope with dynamically varying visual data, we further present an incremental version of ALSD, which is able to incrementally update the NMF solution with a low computational cost. Experimental results demonstrate the efficiency and scalability of the proposed methods as well as their applications to image clustering and image retrieval.
Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization
Wang, Jim Jing-Yan
2014-02-01
Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm - multiple-domain NMF (MDNMF) - was evaluated on two challenging multiple-domain learning problems - multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. © 2013 Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Shah, Ghafoor; Koch, Peter; Papadias, Constantinos B.
2014-01-01
. A novel method based on hierarchical decomposition of the single channel mixture using various nonnegative matrix factorization techniques is proposed, which provides unsupervised clustering of the underlying component signals. HRV is determined over the recovered normal cardiac acoustic signals....... This novel decomposition technique is compared against the state-of-the-art techniques; experiments are performed using real-world clinical data, which show the potential significance of the proposed technique....
Institute of Scientific and Technical Information of China (English)
Xiu-rui GENG; Lu-yan JI; Kang SUN
2016-01-01
Non-negative matrix factorization (NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings: (1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise (outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis (PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and fi nd that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF’ (PCNMF). Experimental results show that PCNMF is both accurate and time-saving.
Minimum-volume-constrained nonnegative matrix factorization: enhanced ability of learning parts.
Zhou, Guoxu; Xie, Shengli; Yang, Zuyuan; Yang, Jun-Mei; He, Zhaoshui
2011-10-01
Nonnegative matrix factorization (NMF) with minimum-volume-constraint (MVC) is exploited in this paper. Our results show that MVC can actually improve the sparseness of the results of NMF. This sparseness is L(0)-norm oriented and can give desirable results even in very weak sparseness situations, thereby leading to the significantly enhanced ability of learning parts of NMF. The close relation between NMF, sparse NMF, and the MVC_NMF is discussed first. Then two algorithms are proposed to solve the MVC_NMF model. One is called quadratic programming_MVC_NMF (QP_MVC_NMF) which is based on quadratic programming and the other is called negative glow_MVC_NMF (NG_MVC_NMF) because it uses multiplicative updates incorporating natural gradient ingeniously. The QP_MVC_NMF algorithm is quite efficient for small-scale problems and the NG_MVC_NMF algorithm is more suitable for large-scale problems. Simulations show the efficiency and validity of the proposed methods in applications of blind source separation and human face images analysis.
Wang, Jim Jing-Yan
2014-09-20
Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.
UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization.
Choo, Jaegul; Lee, Changhyun; Reddy, Chandan K; Park, Haesun
2013-12-01
Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
Zhu, Xun; Ching, Travers; Pan, Xinghua; Weissman, Sherman M.
2017-01-01
Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at: https://github.com/lanagarmire/NMFEM. PMID:28133571
Nonnegative Matrix Factorization-Based Spatial-Temporal Clustering for Multiple Sensor Data Streams
Directory of Open Access Journals (Sweden)
Di-Hua Sun
2014-01-01
Full Text Available Cyber physical systems have grown exponentially and have been attracting a lot of attention over the last few years. To retrieve and mine the useful information from massive amounts of sensor data streams with spatial, temporal, and other multidimensional information has become an active research area. Moreover, recent research has shown that clusters of streams change with a comprehensive spatial-temporal viewpoint in real applications. In this paper, we propose a spatial-temporal clustering algorithm (STClu based on nonnegative matrix trifactorization by utilizing time-series observational data streams and geospatial relationship for clustering multiple sensor data streams. Instead of directly clustering multiple data streams periodically, STClu incorporates the spatial relationship between two sensors in proximity and integrates the historical information into consideration. Furthermore, we develop an iterative updating optimization algorithm STClu. The effectiveness and efficiency of the algorithm STClu are both demonstrated in experiments on real and synthetic data sets. The results show that the proposed STClu algorithm outperforms existing methods for clustering sensor data streams.
Finding Imaging Patterns of Structural Covariance via Non-Negative Matrix Factorization
Sotiras, Aristeidis; Resnick, Susan M.; Davatzikos, Christos
2015-01-01
In this paper, we investigate the use of Non-Negative Matrix Factorization (NNMF) for the analysis of structural neuroimaging data. The goal is to identify the brain regions that co-vary across individuals in a consistent way, hence potentially being part of underlying brain networks or otherwise influenced by underlying common mechanisms such as genetics and pathologies. NNMF offers a directly data-driven way of extracting relatively localized co-varying structural regions, thereby transcending limitations of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and other related methods that tend to produce dispersed components of positive and negative loadings. In particular, leveraging upon the well known ability of NNMF to produce parts-based representations of image data, we derive decompositions that partition the brain into regions that vary in consistent ways across individuals. Importantly, these decompositions achieve dimensionality reduction via highly interpretable ways and generalize well to new data as shown via split-sample experiments. We empirically validate NNMF in two data sets: i) a Diffusion Tensor (DT) mouse brain development study, and ii) a structural Magnetic Resonance (sMR) study of human brain aging. We demonstrate the ability of NNMF to produce sparse parts-based representations of the data at various resolutions. These representations seem to follow what we know about the underlying functional organization of the brain and also capture some pathological processes. Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA. PMID:25497684
Non-negative Matrix Factorization as a Method for Studying Coronal Heating
Barnes, Will; Bradshaw, Stephen
2015-04-01
Many theoretical efforts have been made to model the response of coronal loops to nanoflare heating, but the theory has long suffered from a lack of direct observations. Nanoflares, originally proposed by Parker (1988), heat the corona through short, impulsive bursts of energy. Because of their short duration and comparatively low amplitude, emission signatures from nanoflare heating events are often difficult to detect. Past algorithms (e.g. Ugarte-Urra and Warren, 2014) for measuring the frequency of transient brightenings in active region cores have provided only a lower bound for such measurements. We present the use of non-negative matrix factorization (NMF) to analyze spectral data in active region cores in order to provide more accurate determinations of nanoflare heating properties. NMF, a matrix deconvolution technique, has a variety of applications , ranging from Raman spectroscopy to face recognition, but, to our knowledge, has not been applied in the field of solar physics. The strength of NMF lies in its ability to estimate sources (heating events) from measurements (observed spectral emission) without any knowledge of the mixing process (Cichocki et al., 2009). We apply our NMF algorithm to forward-modeled emission representative of that produced by nanoflare heating events in an active region core. The heating events are modeled using a state-of-the-art hydrodynamics code (Bradshaw and Cargill, 2013) and the emission and active regions are synthesized using advanced forward modeling and visualization software (Bradshaw and Klimchuk, 2011; Reep et al., 2013). From these active region visualizations, our NMF algorithm is then able to predict the heating event frequency and amplitudes. Improved methods of nanoflare detection will help to answer fundamental questions regarding the frequency of energy release in the solar corona and how the corona responds to such impulsive heating. Additionally, development of reliable, automated nanoflare detection
Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.
Jia, Zhilong; Zhang, Xiang; Guan, Naiyang; Bo, Xiaochen; Barnes, Michael R; Luo, Zhigang
2015-01-01
RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the additive combination of all genes, while the second learned factor represents the expression values of two metagenes. In the gene ranking stage, all the genes are ranked as a descending sequence according to the differential values of the metagene weights. Leveraging the nature of NMF and Fisher's criterion, DNMF can robustly boost the gene ranking performance. The Area Under the Curve analysis of differential expression analysis on two benchmarking tests of four RNA-seq data sets with similar phenotypes showed that our proposed DNMF-based gene ranking method outperforms other widely used methods. Moreover, the Gene Set Enrichment Analysis also showed DNMF outweighs others. DNMF is also computationally efficient, substantially outperforming all other benchmarked methods. Consequently, we suggest DNMF is an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data.
Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu
2015-05-01
Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.
Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization
Alexandrov, Boian S.; Vesselinov, Velimir V.
2014-09-01
The identification of the physical sources causing spatial and temporal fluctuations of aquifer water levels is a challenging, yet a very important hydrogeological task. The fluctuations can be caused by variations in natural and anthropogenic sources such as pumping, recharge, barometric pressures, etc. The source identification can be crucial for conceptualization of the hydrogeological conditions and characterization of aquifer properties. We propose a new computational framework for model-free inverse analysis of pressure transients based on Nonnegative Matrix Factorization (NMF) method for Blind Source Separation (BSS) coupled with k-means clustering algorithm, which we call NMFk. NMFk is capable of identifying a set of unique sources from a set of experimentally measured mixed signals, without any information about the sources, their transients, and the physical mechanisms and properties controlling the signal propagation through the subsurface flow medium. Our analysis only requires information about pressure transients at a number of observation points, m, where m≥r, and r is the number of unknown unique sources causing the observed fluctuations. We apply this new analysis on a data set from the Los Alamos National Laboratory site. We demonstrate that the sources identified by NMFk have real physical origins: barometric pressure and water-supply pumping effects. We also estimate the barometric pressure efficiency of the monitoring wells. The possible applications of the NMFk algorithm are not limited to hydrogeology problems; NMFk can be applied to any problem where temporal system behavior is observed at multiple locations and an unknown number of physical sources are causing these fluctuations.
Matrix Representation in Quantum Mechanics with Non-Negative QDF in the Case of a Hydrogen-Like Atom
Zhidkov, E P; Lovetsky, K P; Tretiakov, N P
2002-01-01
The correspondence rules A(q,p)\\mapsto\\hat{A} of the orthodoxal quantum mechanics do not allow one to introduce into the theory the non-negative quantum distribution function F(q,p). The correspondence rules A(q,p)\\mapsto\\hat{O}(A) of Kuryshkin's quantum mechanics (QMK) do allow one to do it. Besides, the operators \\hat{O}(A) turn out to be \\hat{A} bounded and \\hat{A} small at infinity for all systems of auxiliary functions {\\varphi_k}. This allows one to realise canonical matrix representation of QMK to investigate its dependence on the systems of functions {\\varphi_k}.
DEFF Research Database (Denmark)
2014-01-01
Due to applications in areas such as diagnostics and environmental safety, detection of molecules at very low concentrations has attracted recent attention. A powerful tool for this is Surface Enhanced Raman Spectroscopy (SERS) where substrates form localized areas of electromagnetic “hot spots...... a Bayesian Non-negative Matrix Factorization (NMF) approach to identify locations of target molecules. The proposed method is able to successfully analyze the spectra and extract the target spectrum. A visualization of the loadings of the basis vector is created and the results show a clear SNR enhancement...
NMF-mGPU: non-negative matrix factorization on multi-GPU systems.
Mejía-Roa, Edgardo; Tabas-Madrid, Daniel; Setoain, Javier; García, Carlos; Tirado, Francisco; Pascual-Montano, Alberto
2015-02-13
In the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster. In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units ( GPUs ). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU. NMF-mGPU is based on CUDA ( Compute Unified Device Architecture ), the NVIDIA's framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system's main memory to the GPU's memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI ( Message Passing Interface ). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup). Applications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the
Nonnegativity of uncertain polynomials
Directory of Open Access Journals (Sweden)
iljak Dragoslav D.
1998-01-01
Full Text Available The purpose of this paper is to derive tests for robust nonnegativity of scalar and matrix polynomials, which are algebraic, recursive, and can be completed in finite number of steps. Polytopic families of polynomials are considered with various characterizations of parameter uncertainty including affine, multilinear, and polynomic structures. The zero exclusion condition for polynomial positivity is also proposed for general parameter dependencies. By reformulating the robust stability problem of complex polynomials as positivity of real polynomials, we obtain new sufficient conditions for robust stability involving multilinear structures, which can be tested using only real arithmetic. The obtained results are applied to robust matrix factorization, strict positive realness, and absolute stability of multivariable systems involving parameter dependent transfer function matrices.
Ding, Xiaoyu; Lee, Jong-Hwan; Lee, Seong-Whan
2013-04-01
Nonnegative matrix factorization (NMF) is a blind source separation (BSS) algorithm which is based on the distinct constraint of nonnegativity of the estimated parameters as well as on the measured data. In this study, according to the potential feasibility of NMF for fMRI data, the four most popular NMF algorithms, corresponding to the following two types of (1) least-squares based update [i.e., alternating least-squares NMF (ALSNMF) and projected gradient descent NMF] and (2) multiplicative update (i.e., NMF based on Euclidean distance and NMF based on divergence cost function), were investigated by using them to estimate task-related neuronal activities. These algorithms were applied firstly to individual data from a single subject and, subsequently, to group data sets from multiple subjects. On the single-subject level, although all four algorithms detected task-related activation from simulated data, the performance of multiplicative update NMFs was significantly deteriorated when evaluated using visuomotor task fMRI data, for which they failed in estimating any task-related neuronal activities. In group-level analysis on both simulated data and real fMRI data, ALSNMF outperformed the other three algorithms. The presented findings may suggest that ALSNMF appears to be the most promising option among the tested NMF algorithms to extract task-related neuronal activities from fMRI data.
Wright, L.; Coddington, O.; Pilewskie, P.
2015-12-01
Current challenges in Earth remote sensing require improved instrument spectral resolution, spectral coverage, and radiometric accuracy. Hyperspectral instruments, deployed on both aircraft and spacecraft, are a growing class of Earth observing sensors designed to meet these challenges. They collect large amounts of spectral data, allowing thorough characterization of both atmospheric and surface properties. The higher accuracy and increased spectral and spatial resolutions of new imagers require new numerical approaches for processing imagery and separating surface and atmospheric signals. One potential approach is source separation, which allows us to determine the underlying physical causes of observed changes. Improved signal separation will allow hyperspectral instruments to better address key science questions relevant to climate change, including land-use changes, trends in clouds and atmospheric water vapor, and aerosol characteristics. In this work, we investigate a Non-negative Matrix Factorization (NMF) method for the separation of atmospheric and land surface signal sources. NMF offers marked benefits over other commonly employed techniques, including non-negativity, which avoids physically impossible results, and adaptability, which allows the method to be tailored to hyperspectral source separation. We adapt our NMF algorithm to distinguish between contributions from different physically distinct sources by introducing constraints on spectral and spatial variability and by using library spectra to inform separation. We evaluate our NMF algorithm with simulated hyperspectral images as well as hyperspectral imagery from several instruments including, the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), NASA Hyperspectral Imager for the Coastal Ocean (HICO) and National Ecological Observatory Network (NEON) Imaging Spectrometer.
Yun, Younghee; Jung, Wonmo; Kim, Hyunho; Jang, Bo-Hyoung; Kim, Min-Hee; Noh, Jiseong; Ko, Seong-Gyu; Choi, Inhwa
2017-08-01
Syndrome differentiation (SD) results in a diagnostic conclusion based on a cluster of concurrent symptoms and signs, including pulse form and tongue color. In Korea, there is a strong interest in the standardization of Traditional Medicine (TM). In order to standardize TM treatment, standardization of SD should be given priority. The aim of this study was to explore the SD, or symptom clusters, of patients with atopic dermatitis (AD) using non-negative factorization methods and k-means clustering analysis. We screened 80 patients and enrolled 73 eligible patients. One TM dermatologist evaluated the symptoms/signs using an existing clinical dataset from patients with AD. This dataset was designed to collect 15 dermatologic and 18 systemic symptoms/signs associated with AD. Non-negative matrix factorization was used to decompose the original data into a matrix with three features and a weight matrix. The point of intersection of the three coordinates from each patient was placed in three-dimensional space. With five clusters, the silhouette score reached 0.484, and this was the best silhouette score obtained from two to nine clusters. Patients were clustered according to the varying severity of concurrent symptoms/signs. Through the distribution of the null hypothesis generated by 10,000 permutation tests, we found significant cluster-specific symptoms/signs from the confidence intervals in the upper and lower 2.5% of the distribution. Patients in each cluster showed differences in symptoms/signs and severity. In a clinical situation, SD and treatment are based on the practitioners' observations and clinical experience. SD, identified through informatics, can contribute to development of standardized, objective, and consistent SD for each disease. Copyright © 2017. Published by Elsevier Ltd.
Soelter, Jan; Schumacher, Jan; Spors, Hartwig; Schmuker, Michael
2014-09-01
Segmentation of functional parts in image series of functional activity is a common problem in neuroscience. Here we apply regularized non-negative matrix factorization (rNMF) to extract glomeruli in intrinsic optical signal (IOS) images of the olfactory bulb. Regularization allows us to incorporate prior knowledge about the spatio-temporal characteristics of glomerular signals. We demonstrate how to identify suitable regularization parameters on a surrogate dataset. With appropriate regularization segmentation by rNMF is more resilient to noise and requires fewer observations than conventional spatial independent component analysis (sICA). We validate our approach in experimental data using anatomical outlines of glomeruli obtained by 2-photon imaging of resting synapto-pHluorin fluorescence. Taken together, we show that rNMF provides a straightforward method for problem tailored source separation that enables reliable automatic segmentation of functional neural images, with particular benefit in situations with low signal-to-noise ratio as in IOS imaging.
Directory of Open Access Journals (Sweden)
Ruiqi Liao
2014-02-01
Full Text Available In the past decades, advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and interpretation. Recently, nonnegative matrix factorization (NMF has been introduced as an efficient way to reduce the complexity of data as well as to interpret them, and has been applied to various fields of biological research. In this paper, we present CloudNMF, a distributed open-source implementation of NMF on a MapReduce framework. Experimental evaluation demonstrated that CloudNMF is scalable and can be used to deal with huge amounts of data, which may enable various kinds of a high-throughput biological data analysis in the cloud. CloudNMF is freely accessible at http://admis.fudan.edu.cn/projects/CloudNMF.html.
Liao, Ruiqi; Zhang, Yifan; Guan, Jihong; Zhou, Shuigeng
2014-02-01
In the past decades, advances in high-throughput technologies have led to the generation of huge amounts of biological data that require analysis and interpretation. Recently, nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data as well as to interpret them, and has been applied to various fields of biological research. In this paper, we present CloudNMF, a distributed open-source implementation of NMF on a MapReduce framework. Experimental evaluation demonstrated that CloudNMF is scalable and can be used to deal with huge amounts of data, which may enable various kinds of a high-throughput biological data analysis in the cloud. CloudNMF is freely accessible at http://admis.fudan.edu.cn/projects/CloudNMF.html.
Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi
2015-01-01
Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.
Directory of Open Access Journals (Sweden)
Bin Ju
Full Text Available Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.
HPC-NMF: A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization
Energy Technology Data Exchange (ETDEWEB)
2016-08-22
NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient distributed algorithms to solve the problem for big data sets. We propose a high-performance distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems for $\\WW$ and $\\HH$. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). As opposed to previous implementation, our algorithm is also flexible: It performs well for both dense and sparse matrices, and allows the user to choose any one of the multiple algorithms for solving the updates to low rank factors $\\WW$ and $\\HH$ within the alternating iterations.
Fontaine, Anne; Hurley, Susan
2011-01-01
This student research project explores the properties of a family of matrices of zeros and ones that arises from the study of the diagonal lengths in a regular polygon. There is one family for each n greater than 2. A series of exercises guides the student to discover the eigenvalues and eigenvectors of the matrices, which leads in turn to…
Chrétien, Stéphane; Guyeux, Christophe; Conesa, Bastien; Delage-Mouroux, Régis; Jouvenot, Michèle; Huetz, Philippe; Descôtes, Françoise
2016-08-31
Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers. An essential property for missing data imputation and detection of outliers is that the uncorrupted data matrix is low rank, i.e. has only a small number of degrees of freedom. We devise a new version of the Bregman proximal idea which preserves nonnegativity and mix it with the Augmented Lagrangian approach for simultaneous reconstruction of the features of interest and detection of the outliers using a sparsity promoting ℓ 1 penality. An application to the analysis of gene expression data of patients with bladder cancer is finally proposed.
Directory of Open Access Journals (Sweden)
Martin R L Paine
Full Text Available High-grade serous carcinoma (HGSC is the most common and deadliest form of ovarian cancer. Yet it is largely asymptomatic in its initial stages. Studying the origin and early progression of this disease is thus critical in identifying markers for early detection and screening purposes. Tissue-based mass spectrometry imaging (MSI can be employed as an unbiased way of examining localized metabolic changes between healthy and cancerous tissue directly, at the onset of disease. In this study, we describe MSI results from Dicer-Pten double-knockout (DKO mice, a mouse model faithfully reproducing the clinical nature of human HGSC. By using non-negative matrix factorization (NMF for the unsupervised analysis of desorption electrospray ionization (DESI datasets, tissue regions are segregated based on spectral components in an unbiased manner, with alterations related to HGSC highlighted. Results obtained by combining NMF with DESI-MSI revealed several metabolic species elevated in the tumor tissue and/or surrounding blood-filled cyst including ceramides, sphingomyelins, bilirubin, cholesterol sulfate, and various lysophospholipids. Multiple metabolites identified within the imaging study were also detected at altered levels within serum in a previous metabolomic study of the same mouse model. As an example workflow, features identified in this study were used to build an oPLS-DA model capable of discriminating between DKO mice with early-stage tumors and controls with up to 88% accuracy.
Kim, Jaeyeon; Bennett, Rachel V.; Parry, R. Mitchell; Gaul, David A.; Wang, May D.; Matzuk, Martin M.; Fernández, Facundo M.
2016-01-01
High-grade serous carcinoma (HGSC) is the most common and deadliest form of ovarian cancer. Yet it is largely asymptomatic in its initial stages. Studying the origin and early progression of this disease is thus critical in identifying markers for early detection and screening purposes. Tissue-based mass spectrometry imaging (MSI) can be employed as an unbiased way of examining localized metabolic changes between healthy and cancerous tissue directly, at the onset of disease. In this study, we describe MSI results from Dicer-Pten double-knockout (DKO) mice, a mouse model faithfully reproducing the clinical nature of human HGSC. By using non-negative matrix factorization (NMF) for the unsupervised analysis of desorption electrospray ionization (DESI) datasets, tissue regions are segregated based on spectral components in an unbiased manner, with alterations related to HGSC highlighted. Results obtained by combining NMF with DESI-MSI revealed several metabolic species elevated in the tumor tissue and/or surrounding blood-filled cyst including ceramides, sphingomyelins, bilirubin, cholesterol sulfate, and various lysophospholipids. Multiple metabolites identified within the imaging study were also detected at altered levels within serum in a previous metabolomic study of the same mouse model. As an example workflow, features identified in this study were used to build an oPLS-DA model capable of discriminating between DKO mice with early-stage tumors and controls with up to 88% accuracy. PMID:27159635
Ghoraani, Behnaz
2016-12-01
Time-frequency (TF) representation has found wide use in many challenging signal processing tasks including classification, interference rejection, and retrieval. Advances in TF analysis methods have led to the development of powerful techniques, which use non-negative matrix factorization (NMF) to adaptively decompose the TF data into TF basis components and coefficients. In this paper, standard NMF is modified for TF data, such that the improved TF bases can be used for signal classification applications with overlapping classes and data retrieval. The new method, called jointly learnt NMF (JLNMF) method, identifies both distinct and shared TF bases and is able to use the decomposed bases to successfully retrieve and separate the class-specific information from data. The paper provides the framework of the proposed JLNMF cost function and proposes a projected gradient framework to solve for limit point stationarity solutions. The developed algorithm has been applied to a synthetic data retrieval experiment and epileptic spikes in EEG signals of infantile spasms and discrimination of pathological voice disorder. The experimental results verified that JLNMF successfully identified the class-specific information, thus enhancing data separation performance.
不完全非负矩阵分解的加速算法%Accelerated Algorithm to Incomplete Nonnegative Matrix Factorization
Institute of Scientific and Technical Information of China (English)
史加荣; 焦李成; 尚凡华
2011-01-01
非负矩阵分解(NMF)已成为数据分析与处理的一种日益流行的方法.当数据矩阵不完全时,可用加权非负矩阵分解(WNMF)来分解矩阵.但是在WNMF算法中,对于给定的搜索方向,步长的选取一般来说不是最优的.本文研究了不完全非负矩阵分解(INMF)问题,提出了加速算法(AINMF).首先,将INMF问题转化为交替地求解两个非负最小二乘(NNNLS)问题.对于每个NNLS问题,在搜索方向上采用精确的步长.接着,分析了NNLS问题的算法复杂度.最后,试验结果证实了AINMF优于WNMF.%Nornegative matrix factorization (NMF) is an increasingly popular technique for data processing and analysis. For an incomplete data matrix, the weighted nonnegative matrix factorization (WNMF) is employed to decompose it. But the searching step size in WNMF is not optimal along the given seaching direction. This paper studies the incomplete nonnegative matrix factorization (INMF) and proposes an accelerated algorithm. First, INMF is transformed into solving alternatively two nonnegative least squares (NNLS) problems. For each NNLS problem, the exact step size is chosen along the searching direction. Then, the complexity of NNLS problems is analyzed. Finally, experimental results show that the proposed method outperforms WNMF.
Strictly nonnegative tensors and nonnegative tensor partition
Institute of Scientific and Technical Information of China (English)
HU ShengLong; HUANG ZhengHai; QI LiQun
2014-01-01
We introduce a new class of nonnegative tensors—strictly nonnegative tensors.A weakly irreducible nonnegative tensor is a strictly nonnegative tensor but not vice versa.We show that the spectral radius of a strictly nonnegative tensor is always positive.We give some necessary and su？cient conditions for the six wellconditional classes of nonnegative tensors,introduced in the literature,and a full relationship picture about strictly nonnegative tensors with these six classes of nonnegative tensors.We then establish global R-linear convergence of a power method for finding the spectral radius of a nonnegative tensor under the condition of weak irreducibility.We show that for a nonnegative tensor T,there always exists a partition of the index set such that every tensor induced by the partition is weakly irreducible;and the spectral radius of T can be obtained from those spectral radii of the induced tensors.In this way,we develop a convergent algorithm for finding the spectral radius of a general nonnegative tensor without any additional assumption.Some preliminary numerical results show the feasibility and effectiveness of the algorithm.
Liu, Xu; Liu, Tiao-Tiao; Bai, Wen-Wen; Yi, Hu; Li, Shuang-Yan; Tian, Xin
2013-06-01
Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multi-channel local field potentials (LFPs) based on sparse nonnegative matrix factorization (SNMF). SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four Sprague-Dawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the power-increased LFP components were selected as working memory-related features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time-frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.
DEFF Research Database (Denmark)
Mørup, Morten; Hansen, Lars Kai; Parnas, Josef;
2006-01-01
generalized to a parallel factor (PARAFAC) model to form a non-negative multi-way factorization (NMWF). While the NMF can examine subject specific activities the NMWF can effectively extract the most similar activities across subjects and or conditions. The methods are tested on a proprioceptive stimulus...... consisting of a weight change in a handheld load. While somatosensory gamma oscillations have previously only been evoked by electrical stimuli we hypothesized that a natural proprioceptive stimulus also would be able to evoke gamma oscillations. ITPC maxima were determined by visual inspection...... contralateral to stimulus side and additionally an unexpected 20Hz activity slightly lateralized in the frontal central region. Consequently, also proprioceptive stimuli are able to elicit evoked gamma activity....
Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E
2017-04-15
Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (pcoding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (pcoding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.
Moghadam, M Nassajian; Aminian, K; Asghari, M; Parnianpour, M
2013-01-01
The way central nervous system manages the excess degrees of freedom to solve kinetic redundancy of musculoskeletal system remains an open question. In this study, we utilise the concept of synergy formation as a simplifying control strategy to find the muscle recruitment based on summation of identified muscle synergies to balance the biomechanical demands (biaxial external torque) during an isometric shoulder task. A numerical optimisation-based shoulder model was used to obtain muscle activation levels when a biaxial external isometric torque is imposed at the shoulder glenohumeral joint. In the numerical simulations, 12 different shoulder torque vectors in the transverse plane are considered. For each selected direction for the torque vector, the resulting muscle activation data are calculated. The predicted muscle activation data are used for grouping muscles in some fixed element synergies by the non-negative matrix factorisation method. Next, torque produced by these synergies are computed and projected in the 2D torque space to investigate the magnitude and direction of torques that each muscle synergy generated. The results confirmed our expectation that few dominant synergies are sufficient to reconstruct the torque vectors and each muscle contributed to more than one synergy. Decomposition of the concatenated data, combining the activation and external torque, provided functional muscle synergies that produced torques in the four principal directions. Four muscle synergies were able to account for more than 95% of variation of the original data.
Wang, T.; Zhang, H.; Lin, H.
2017-09-01
surfaces has increasingly roused widely interests of researchers in monitoring urban development and determining the overall environmental health of a watershed. However, studies on the impervious surface using multi-spectral imageries is insufficient and inaccurate due to the complexity of urban infrastructures base on the need to further recognize these impervious surface materials in a finer scale. Hyperspectral imageries have been proved to be sensitive to subtle spectral differences thus capable to exquisitely discriminate these similar materials while limited to the low spatial resolution. Coupled nonnegative matrix factorization (CNMF) unmixing method is one of the most physically straightforward and easily complemented hyperspectral pan-sharpening methods that could produce fused data with both high spectral and spatial resolution. This paper aimed to exploit the latent capacity and tentative validation of CNMF on the killer application of mapping urban impervious surfaces in complexed metropolitan environments like Hong Kong. Experiments showed that the fusion of high spectral and spatial resolution image could provide more accurate and comprehensive information on urban impervious surface estimation.
Long, C J; Bunker, D; Li, X; Karen, V L; Takeuchi, I
2009-10-01
In this work we apply a technique called non-negative matrix factorization (NMF) to the problem of analyzing hundreds of x-ray microdiffraction (microXRD) patterns from a combinatorial materials library. An in-house scanning x-ray microdiffractometer is used to obtain microXRD patterns from 273 different compositions on a single composition spread library. NMF is then used to identify the unique microXRD patterns present in the system and quantify the contribution of each of these basis patterns to each experimental diffraction pattern. As a baseline, the results of NMF are compared to the results obtained using principle component analysis. The basis patterns found using NMF are then compared to reference patterns from a database of known structural patterns in order to identify known structures. As an example system, we explore a region of the Fe-Ga-Pd ternary system. The use of NMF in this case reduces the arduous task of analyzing hundreds of microXRD patterns to the much smaller task of identifying only nine microXRD patterns.
Thiem, A.; Schlink, U.; Pan, X.-C.; Hu, M.; Peters, A.; Wiedensohler, A.; Breitner, S.; Cyrys, J.; Wehner, B.; Rösch, C.; Franck, U.
2012-05-01
Increasing traffic density and a changing car fleet on the one hand as well as various reduction measures on the other hand may influence the composition of the particle population and, hence, the health risks for residents of megacities like Beijing. A suitable tool for identification and quantification of source group-related particle exposure compositions is desirable in order to derive optimal adaptation and reduction strategies and therefore, is presented in this paper. Particle number concentrations have been measured in high time- and space-resolution at an urban background monitoring site in Beijing, China, during 2004-2008. In this study a new pattern recognition procedure based on non-negative matrix factorization (NMF) was introduced to extract characteristic diurnal air pollution patterns of particle number and volume size distributions for the study period. Initialization and weighting strategies for NMF applications were carefully considered and a scaling procedure for ranking of obtained patterns was implemented. In order to account for varying particle sizes in the full diameter range [3 nm; 10 μm] two separate NMF applications (a) for diurnal particle number concentration data (NMF-N) and (b) volume concentration data (NMF-V) have been performed. Five particle number concentration-related NMF-N factors were assigned to patterns mainly describing the development of ultrafine (particle diameter Dp < 100 nm instead of DP) as well as fine particles (Dp < 2.5 μm), since absolute number concentrations are highest in these diameter ranges. The factors are classified into primary and secondary sources. Primary sources mostly involved anthropogenic emission sources such as traffic emissions or emissions of nearby industrial plants, whereas secondary sources involved new particle formation and accumulation (particle growth) processes. For the NMF-V application the five extracted factors mainly described coarse particle (2.5 μm < Dp < 10 μm) variations
Directory of Open Access Journals (Sweden)
Mark Lutman
2013-10-01
Full Text Available Cochlear implants (CIs require efficient speech processing to maximize information transmission to the brain, especially in noise. A novel CI processing strategy was proposed in our previous studies, in which sparsity-constrained non-negative matrix factorization (NMF was applied to the envelope matrix in order to improve the CI performance in noisy environments. It showed that the algorithm needs to be adaptive, rather than fixed, in order to adjust to acoustical conditions and individual characteristics. Here, we explore the benefit of a system that allows the user to adjust the signal processing in real time according to their individual listening needs and their individual hearing capabilities. In this system, which is based on MATLABR , SIMULINKR and the xPC TargetTM environment, the input/outupt (I/O boards are interfaced between the SIMULINK blocks and the CI stimulation system, such that the output can be controlled successfully in the manner of a hardware-in-the-loop (HIL simulation, hence offering a convenient way to implement a real time signal processing module that does not require any low level language. The sparsity constrained parameter of the algorithm was adapted online subjectively during an experiment with normal-hearing subjects and noise vocoded speech simulation. Results show that subjects chose different parameter values according to their own intelligibility preferences, indicating that adaptive real time algorithms are beneficial to fully explore subjective preferences. We conclude that the adaptive real time systems are beneficial for the experimental design, and such systems allow one to conduct psychophysical experiments with high ecological validity.
Ray, Sumanta; Maulik, Ujjwal
2016-12-20
Detecting perturbation in modular structure during HIV-1 disease progression is an important step to understand stage specific infection pattern of HIV-1 virus in human cell. In this article, we proposed a novel methodology on integration of multiple biological information to identify such disruption in human gene module during different stages of HIV-1 infection. We integrate three different biological information: gene expression information, protein-protein interaction information and gene ontology information in single gene meta-module, through non negative matrix factorization (NMF). As the identified metamodules inherit those information so, detecting perturbation of these, reflects the changes in expression pattern, in PPI structure and in functional similarity of genes during the infection progression. To integrate modules of different data sources into strong meta-modules, NMF based clustering is utilized here. Perturbation in meta-modular structure is identified by investigating the topological and intramodular properties and putting rank to those meta-modules using a rank aggregation algorithm. We have also analyzed the preservation structure of significant GO terms in which the human proteins of the meta-modules participate. Moreover, we have performed an analysis to show the change of coregulation pattern of identified transcription factors (TFs) over the HIV progression stages.
ON CONVERGENCE OF MULTIGRID METHOD FOR NONNEGATIVE DEFINITE SYSTEMS
Institute of Scientific and Technical Information of China (English)
Qian-shun Chang; Wei-wei Sun
2005-01-01
In this paper, we consider multigrid methods for solving symmetric nonnegative definite matrix equations. We present some interesting features of the multigrid method and prove that the method is convergent in L2 space and the convergent solution is unique for such nonnegative definite system and given initial guess.
Vesselinov, V. V.; Alexandrov, B.
2014-12-01
The identification of the physical sources causing spatial and temporal fluctuations of state variables such as river stage levels and aquifer hydraulic heads is challenging. The fluctuations can be caused by variations in natural and anthropogenic sources such as precipitation events, infiltration, groundwater pumping, barometric pressures, etc. The source identification and separation can be crucial for conceptualization of the hydrological conditions and characterization of system properties. If the original signals that cause the observed state-variable transients can be successfully "unmixed", decoupled physics models may then be applied to analyze the propagation of each signal independently. We propose a new model-free inverse analysis of transient data based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS) coupled with k-means clustering algorithm, which we call NMFk. NMFk is capable of identifying a set of unique sources from a set of experimentally measured mixed signals, without any information about the sources, their transients, and the physical mechanisms and properties controlling the signal propagation through the system. A classical BSS conundrum is the so-called "cocktail-party" problem where several microphones are recording the sounds in a ballroom (music, conversations, noise, etc.). Each of the microphones is recording a mixture of the sounds. The goal of BSS is to "unmix'" and reconstruct the original sounds from the microphone records. Similarly to the "cocktail-party" problem, our model-freee analysis only requires information about state-variable transients at a number of observation points, m, where m > r, and r is the number of unknown unique sources causing the observed fluctuations. We apply the analysis on a dataset from the Los Alamos National Laboratory (LANL) site. We identify and estimate the impact and sources are barometric pressure and water-supply pumping effects. We also estimate the
Jiang, Jonathan Q
2011-01-01
We show here that the problem of maximizing a family of quantitative functions, encompassing both the modularity (Q-measure) and modularity density (D-measure), for community detection can be uniformly understood as a combinatoric optimization involving the trace of a matrix called modularity Laplacian. Instead of using traditional spectral relaxation, we apply additional nonnegative constraint into this graph clustering problem and design efficient algorithms to optimize the new objective. With the explicit nonnegative constraint, our solutions are very close to the ideal community indicator matrix and can directly assign nodes into communities. The near-orthogonal columns of the solution can be reformulated as the posterior probability of corresponding node belonging to each community. Therefore, the proposed method can be exploited to identify the fuzzy or overlapping communities and thus facilitates the understanding of the intrinsic structure of networks. Experimental results show that our new algorithm ...
Torus-invariant prime ideals in quantum matrices, totally nonnegative cells and symplectic leaves
Goodearl, K R; Lenagan, T H
2009-01-01
The algebra of quantum matrices of a given size supports a rational torus action by automorphisms. It follows from work of Letzter and the first named author that to understand the prime and primitive spectra of this algebra, the first step is to understand the prime ideals that are invariant under the torus action. In this paper, we prove that a family of quantum minors is the set of all quantum minors that belong to a given torus-invariant prime ideal of a quantum matrix algebra if and only if the corresponding family of minors defines a non-empty totally nonnegative cell in the space of totally nonnegative real matrices of the appropriate size. As a corollary, we obtain explicit generating sets of quantum minors for the torus-invariant prime ideals of quantum matrices in the case where the quantisation parameter $q$ is transcendental over $\\mathbb{Q}$.
ON THE MINIMAL NONNEGATIVE SOLUTION OF NONSYMMETRIC ALGEBRAIC RICCATI EQUATION
Institute of Scientific and Technical Information of China (English)
Xiao-xia Guo; Zhong-zhi Bai
2005-01-01
We study perturbation bound and structured condition number about the minimal nonnegative solution of nonsymmetric algebraic Riccati equation, obtaining a sharp perturbation bound and an accurate condition number. By using the matrix sign function method we present a new method for finding the minimal nonnegative solution of this algebraic Riccati equation. Based on this new method, we show how to compute the desired M-matrix solution of the quadratic matrix equation X2 - EX - F -= 0 by connecting it with the nonsymmetric algebraic Riccati equation, where E is a diagonal matrix and F is an M-matrix.
Institute of Scientific and Technical Information of China (English)
李永华; 唐先超
2014-01-01
近年来国内民航迅速发展，机场的新建、扩建和航空运输量的持续增长使得机场噪声污染事件不仅持续上升，而且噪声污染程度也日益加重，因而强化机场附近噪声污染的监测对机场建设及其环境评估十分重要。针对机场噪声污染监测问题，提出一种基于非负矩阵分解(NMF)方法对机场噪声监测点布局问题进行优化求解。该方法以大量网格点作为候选监测点，对单个飞机噪声事件候选监测点的噪声值所形成的矩阵按非负矩阵分解进行区域划分，得到噪声影响子区域。进一步以各子区域的中心点作为该区域的噪声影响代表点，以此确定机场噪声监测点数目和位置。研究结果表明所获得的解比贪心算法得到的解更优，需要的监测点更少。%With the rapid development of domestic civil aviation in recent years, noise pollution of airports is becoming a serious problem. Thus, strengthening the noise monitoring in the airport vicinity is very important for airport construction and environmental evaluation. Aiming at the monitoring of airport noise pollution, an optimization method based on non-negative matrix factorization (NMF) is put forward to optimize the layout of the noise monitoring sites. In this method, large number of the grid nodes is employed as the candidate monitoring points, and the non-negative matrix composed of the noise values from single flight event at the candidate monitoring points is formed. Then, the non-negative matrix is factorized to obtain the effective subdivision of noise. Furthermore, the location and number of the noise monitoring sites are determined by the representative central point of the effective subdivisions. It is shown that this method needs fewer monitoring locating sites and can get better results than the greedy algorithm.
Energy Technology Data Exchange (ETDEWEB)
Lee, Dong-Chang [CancerCare Manitoba, Winnipeg, MB (Canada); Jans, Hans; McEwan, Sandy; Riauka, Terence [Department of Oncology, University of Alberta, Edmonton, AB (Canada); Cross Cancer Institute, Alberta Health Services, Edmonton, AB (Canada); Martin, Wayne; Wieler, Marguerite [Division of Neurology, University of Alberta, Edmonton, AB (Canada)
2014-08-15
In this work, a class of non-negative matrix factorization (NMF) technique known as alternating non-negative least squares, combined with the projected gradient method, is used to analyze twenty-five [{sup 11}C]-DTBZ dynamic PET/CT brain data. For each subject, a two-factor model is assumed and two factors representing the striatum (factor 1) and the non-striatum (factor 2) tissues are extracted using the proposed NMF technique and commercially available factor analysis software “Pixies”. The extracted factor 1 and 2 curves represent the binding site of the radiotracer and describe the uptake and clearance of the radiotracer by soft tissues in the brain, respectively. The proposed NMF technique uses prior information about the dynamic data to obtain sample time-activity curves representing the striatum and the non-striatum tissues. These curves are then used for “warm” starting the optimization. Factor solutions from the two methods are compared graphically and quantitatively. In healthy subjects, radiotracer uptake by factors 1 and 2 are approximately 35–40% and 60–65%, respectively. The solutions are also used to develop a factor-based metric for the detection of early, untreated Parkinson's disease. The metric stratifies healthy subjects from suspected Parkinson's patients (based on the graphical method). The analysis shows that both techniques produce comparable results with similar computational time. The “semi-automatic” approach used by the NMF technique allows clinicians to manually set a starting condition for “warm” starting the optimization in order to facilitate control and efficient interaction with the data.
Fault detection method based on sparse non-negative matrix factorization%基于稀疏性非负矩阵分解的故障监测方法
Institute of Scientific and Technical Information of China (English)
王帆; 杨雅伟; 谭帅; 侍洪波
2015-01-01
In this paper, a novel fault detection method based on sparse non-negative matrix factorization (SNMF) is proposed. NMF (non-negative matrix factorization) is a new dimension reduction technique that can find a low-rank matrix approximation from the original data. In contrast to the conventional multivariate statistical process monitoring methods, for example PCA, NMF has no assumption about the nature of latent variables, except for non-negativity. Combining linear sparse coding and NMF, SNMF can learn much sparser representation via imposing sparseness constraints. During factorization, low-rank matrix is orthogonalized to remove redundant information and concentrate information on fewer directions of projection. Then, SNMF is used to extract the latent variables that drive a process and new statistical metrics are defined for fault detection. Kernel density estimation (KDE) is adopted to calculate the confidence limits of defined statistical metrics. Afterwards, the proposed method is applied to the Tennessee Eastman process to evaluate the monitoring performance, comparing with conventional NMF and PCA. The results from the experiment show the feasibility of the new method.%提出了基于稀疏性非负矩阵分解（SNMF）的故障监测方法。非负矩阵分解（NMF）是一种新的降维方法，可以得到原始数据的低秩近似矩阵。与传统的多元统计过程监控方法如主成分分析（PCA）相比，NMF对潜变量的性质没有假设，除了非负性的要求。将稀疏编码和非负矩阵分解方法结合在一起，因为施加了稀疏性的约束，稀疏性非负矩阵分解方法可以得到对数据更稀疏的表示。在分解时对低秩近似矩阵进行正交化处理，从而在降维时除去变量中的冗余信息，将信息集中到更少的投影方向上。然后，用SNMF方法来提取过程的潜变量，并定义新的监测指标来进行故障监测。使用核密度估计（KDE）方法来计算新
The difference between 5 x 5 doubly nonnegative and completely positive matrices
Burer, Samuel; Anstreicher, Kurt M.; Duer, Mirjam
2009-01-01
The convex cone of n x n completely positive (CP) matrices and its dual cone of copositive matrices arise in several areas of applied mathematics, including optimization. Every CP matrix is doubly nonnegative (DNN), i.e., positive semidefinite and component-wise nonnegative, and it is known that, fo
The difference between 5 × 5 doubly nonnegative and completely positive matrices
Burer, Samuel; Anstreicher, Kurt M.; Dür, Mirjam
2009-01-01
The convex cone of n × n completely positive (CP) matrices and its dual cone of copositive matrices arise in several areas of applied mathematics, including optimization. Every CP matrix is doubly nonnegative (DNN), i.e., positive semidefinite and component-wise nonnegative, and it is known that, fo
The difference between 5 × 5 doubly nonnegative and completely positive matrices
Burer, Samuel; Anstreicher, Kurt M.; Dür, Mirjam
2009-01-01
The convex cone of n × n completely positive (CP) matrices and its dual cone of copositive matrices arise in several areas of applied mathematics, including optimization. Every CP matrix is doubly nonnegative (DNN), i.e., positive semidefinite and component-wise nonnegative, and it is known that,
The difference between 5 x 5 doubly nonnegative and completely positive matrices
Burer, Samuel; Anstreicher, Kurt M.; Duer, Mirjam
2009-01-01
The convex cone of n x n completely positive (CP) matrices and its dual cone of copositive matrices arise in several areas of applied mathematics, including optimization. Every CP matrix is doubly nonnegative (DNN), i.e., positive semidefinite and component-wise nonnegative, and it is known that,
Family learning research in museums: An emerging disciplinary matrix?
Ellenbogen, Kirsten M.; Luke, Jessica J.; Dierking, Lynn D.
2004-07-01
Thomas Kuhn's notion of a disciplinary matrix provides a useful framework for investigating the growth of research on family learning in and from museums over the last decade. To track the emergence of this disciplinary matrix we consider three issues. First are shifting theoretical perspectives that result in new shared language, beliefs, values, understandings, and assumptions about what counts as family learning. Second are realigning methodologies, driven by underlying disciplinary assumptions about how research in this arena is best conducted, what questions should be addressed, and criteria for valid and reliable evidence. Third is resituating the focus of our research to make the family central to what we study, reflecting a more holistic understanding of the family as an educational institution within larger learning infrastructure. We discuss research that exemplifies these three issues and demonstrates the ways in which shifting theoretical perspectives, realigning methodologies, and resituating research foci signal the existence of a nascent disciplinary matrix.
Institute of Scientific and Technical Information of China (English)
贾伟
2016-01-01
数控机床的切削加工工艺参数选择对零件加工有着重要影响，针对现有工艺参数选择方法的不足之处，提出了一种基于非负矩阵分解的工艺参数选择方法，在矩阵分解中使用贝叶斯准则和Gibbs采样计算后验概率分布，实例分析表明该方法能克服现有方法的不足，实现了对工艺参数的优化选择。%The selection of technological parameters of cutting process in numerical control machine has important influence on the components processing. Some faults of the existing methods of selection of technological parameters are pointed out. A method of selection of technological parameters based on non-negative matrix factorization is proposed. The method calcul-ates the posterior probability distribution by using Bayesian criteria and Gibbs sampling. An example shows that the new meth-od can overcome the shortcomings of the existing methods and realize the optimization of technological parameters.
Update of human and mouse matrix metalloproteinase families
Jackson Brian C; Nebert Daniel W; Vasiliou Vasilis
2010-01-01
Abstract Matrix metalloproteinases (MMPs) are a family of zinc proteases that degrade most of the components of the extracellular matrix (ECM). MMPs also have a number of non-traditional roles in processing factors related to cell growth/proliferation, inflammation and more. There are 23 human MMPs and 23 mouse MMPs, most of which share orthology among most vertebrates; other examples have been found in invertebrates and plants. MMPs are named in order of discovery, but also have been grouped...
General Neutrino Mass Matrix Patterns and Its Underlying Family Symmetries
Damanik, Asan; Anggraita, Pramudita; Muslim,
2014-01-01
Baseon on current experimental results, such as neutrino oscillations and the neutrinoless double beta decays (i.e. data from Super Kamiokande, KamLAND, SNO, etc.), the neutrino mixing matrix can be adequately determined. Though there are still certain parameters that have possibility limits, but based on the current experimental results it is possible to construct a general form of neutrino mass matrix. Starting from this general form of the neutrino mass matrix we put certain conditions in the context of the seesaw mechanism model to determine the possible pattern of the neutrino mass matrix that has a texture zero. From the obtained neutrino mass matrix pattern, there are three class of patterns, where two of the class are known to be realized in literature by the underlying family symmetries of the $D_{4}$ and $A_{4}$ groups, the dihedral and tetrahedral symmetry groups.
On the nonnegative inverse eigenvalue problem of traditional matrices
Directory of Open Access Journals (Sweden)
Alimohammad Nazari
2014-07-01
Full Text Available In this paper, at first for a given set of real or complex numbers $\\sigma$ with nonnegativesummation, we introduce some special conditions that with them there is no nonnegativetridiagonal matrix in which $\\sigma$ is its spectrum. In continue we present some conditions forexistence such nonnegative tridiagonal matrices.
Multivariate refinement equation with nonnegative masks
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
This paper is concerned with multivariate refinement equations of the type ψ = ∑α∈Zs a(α)ψ(Mx - α),where ψ is the unknown function defined on the s-dimensional Euclidean space Rs, a is a finitely supported nonnegative sequence on Zs, and M is an s × s dilation matrix with m := |detM|. We characterize the existence of L2-solution of refinement equation in terms of spectral radius of a certain finite matrix or transition operator associated with refinement mask a and dilation matrix M. For s = 1 and M = 2, the sufficient and necessary conditions are obtained to characterize the existence of continuous solution of this refinement equation.
Alternative quadratic programming for non-negative matrix low-order factorization%非负矩阵低秩分解的交替二次规划算法
Institute of Scientific and Technical Information of China (English)
阳明盛; 刘力军
2014-01-01
非负矩阵分解算法有多种，但都存在着各自的缺陷。在现有工作的基础上，将非负矩阵分解(NMF)模型转化为一组(两个)二次凸规划模型，利用二次凸规划有解的充分必要条件推导出迭代公式，进行交替迭代，可求出问题的解。得到的解不仅具有某种最优性、稀疏性，还避免了约束非线性规划求解的复杂过程和大量的计算。证明了迭代的收敛性，且收敛速度快于已知的方法，对于大规模数据模型尤能显示出其优越性。%Many algorithms are available for solving the problem of non-negative matrix factorization (NMF)despite respective shortcomings.Based on existing works,NMF model is transformed into one group of (two ) convex quadratic programming model. Using the sufficient and necessary conditions for quadratic programming problems,iteration formula for NMF is obtained by which the problem is solved after alternative iteration process.The obtained solution reaches its optimality and sparseness while avoiding computational burden and complexity for solving constrained nonlinear programming problems.The iteration convergence can be proved easily and its speed is faster than that of existing approaches.The proposed approach has its superority for large-scale data model.
Image fusion algorithm based on NSCT and non-negative matrix factorization%NSCT和非负矩阵分解的图像融合方法
Institute of Scientific and Technical Information of China (English)
李美丽; 李言俊; 王红梅; 张科
2010-01-01
非采样Contourlet变换(Nonsubsampled Contourlet transform,NSCT)是一种新的多尺度变换,它同时具有方向性、各向异性和平移不变性,能有效地表示图像的边沿与轮廓.非负矩阵分解(Non-negative Matrix Factorization,NMF)是在矩阵中所有元素均为非负数的条件下的一种矩阵分解方法.在非负矩阵分解过程中,适当地选取特征空间的维数能够获得原始数据的局部特征.提出了一种基于NSCT和NMF的图像融合方法.首先用NSCT对已配准的源图像进行分解,得到低通子带系数和各带通子带系数;其次将低通子带系数作为原始数据,选取特征空间的维数为1,利用非负矩阵分解得到包含特征基的低通子带系数;对各带通子带系数采取绝对值最大的原则进行系数选择,得到融合图像的各带通子带系数;最后经过NSCT逆变换得到融合图像.实验结果表明,融合结果优于Laplacian方法、小渡方法和NMF方法.
Nonnegative and Compartmental Dynamical Systems
Haddad, Wassim M; Hui, Qing
2010-01-01
This comprehensive book provides the first unified framework for stability and dissipativity analysis and control design for nonnegative and compartmental dynamical systems, which play a key role in a wide range of fields, including engineering, thermal sciences, biology, ecology, economics, genetics, chemistry, medicine, and sociology. Using the highest standards of exposition and rigor, the authors explain these systems and advance the state of the art in their analysis and active control design. Nonnegative and Compartmental Dynamical Systems presents the most complete treatment available o
Form Sums of Nonnegative Selfadjoint Operators
Hassi, S.; Sandovici, A.; Snoo, H.S.V. de; Winkler, Henrik; Sandovici, 27740
2006-01-01
The sum of two unbounded nonnegative selfadjoint operators is a nonnegative operator which is not necessarily densely defined. In general its selfadjoint extensions exist in the sense of linear relations (multivalued operators). One of its nonnegative selfadjoint extensions is constructed via the fo
Institute of Scientific and Technical Information of China (English)
栾佳雨; 王海瑞; 毕贵红; 王曦; 陈仕龙
2013-01-01
A combination of phase space reconstruction and Non-negative Matrix Factorization (NMF) methods is applied to recognize six disturbance signals including voltage sag, voltage swell, voltage spikes, voltage interruption, harmonics and fluctuation signals. The phase space reconstruction method is used to construct disturbance signal trajectories converted into images. In the view of image processing, the principle of NMF in the face and fingerprint image recognition is adopted to extract the features of different phase space reconstruction trajectories, and recognize the corresponding power quality disturbance signals. This method can avoid obtaining the difficulties of stability feature extraction which result of the complexity of the disturbance signal, with the training time is short, less training samples needed to identify the process of visualization to facilitate the analysis and so on. Simulation results show that it can better identify the power quality disturbance. It is disturbance signal detection and classification of possible algorithms.%利用相空间重构及非负矩阵分解(NMF)相结合的方法,对电压暂降、电压暂升、电压尖峰、电压中断、暂态谐波及暂态振荡6类电能扰动信号进行分类识别研究.利用相空间重构法构造扰动信号轨迹,并将其转化为图像.从图像处理的角度出发,利用NMF在人脸、指纹图像识别应用中的基本原理,对不同的相空间重构轨迹图进行特征提取,分类识别其所对应的电能质量扰动信号.该方法可避免由于扰动信号的复杂性而难以获得扰动信号稳定特征提取的困难,具有训练时间短、所需训练样本少、识别过程可视化便于分析等特点.仿真实验结果表明其能够较好地识别电能质量扰动,是提供了扰动信号检测与分类的算法.
基于非负矩阵分解的缺失数据插补算法研究%Nonnegative Matrix Factorization-based Missing Data Interpolation Algorithm
Institute of Scientific and Technical Information of China (English)
韩婧; 赵楠
2013-01-01
Supply chain performance depends on the real supply chain performance data. Because of lots of node enterprises, the management consulting commonly used questionnaires, interviews and other research methods cannot meet the actual needs, the data quality of supply chain performance diagnostic a-nalysis is difficult to guarantee. Focus on the interpolation of missing values in the sample data, mainly for the general lack of pattern in the missing data problem, especially for supply chain performance data missing. Based on the missing data processing technology, decomposing matrix theory, and data mining technology, effective imputation algorithm has been given, which based on the decomposition of the nonnegative matrices factorization. The effectiveness of the algorithm is demonstrated by the numerical experiments. The algorithm can be more efficient, accurate, low-cost to get performance data on each node in the supply chain enterprises.%供应链绩效分析依赖于真实的供应链绩效数据.对于供应链绩效诊断分析问题,由于节点企业众多,管理咨询常用的发放问卷、访谈等调研方式难以满足实际需求,数据质量也难以保证.重点探讨样本数据中的缺失值插补问题,主要面向缺失数据问题中的一般缺失模式,特别针对供应链绩效数据缺失问题.基于缺失数据处理技术及矩阵分解理论,给出有效的插补算法:基于非负矩阵分解的插补算法,通过数值实验证明了算法的有效性.该算法可以更为有效、准确、低成本地获得供应链上各节点企业的绩效数据.
Institute of Scientific and Technical Information of China (English)
邓晓政; 焦李成; 卢山
2011-01-01
In this paper,a novel method based on spectral clustering ensemble using nonnegative matrix factorization (NMF) is proposed for the segmentation of SAR image. Firstly, diversity segmentation components are obtained due to the spectral clustering method is sensitive to the scaling parameter. Secondly, these components are combined by using NMF, NMF is a method that can obtain a representation of data full of intuitive meaning and physical interpretation. Finally, segmentation result is obtained according to the combined result.To show the effectiveness of the novel method, experiments with three texture images and four SAR images are considered. The segmentation results are evaluated by comparing with K-means method, spectral clustering method based on Nystrom approximation and Meta-clustering method. According to the qualitative and quantitative analysis, the proposed method is effective and has some practical value.%本文提出了一种新颖的基于非负矩阵分解的谱聚类集成SAR图像分割框架.首先,个体分割结果的产生采用基于Nystrom逼近的谱聚类方法,使用不同的尺度参数,得到具有差异性的个体分割结果；其次,使用非负矩阵分解的方法来合并这些个体分割结果,使用非负矩阵分解方法的优点在于其合乎人类大脑感知的直观体验,并具有明确的物理含义；最后,根据合并得到的像素点隶属度关系得到SAR图像分割结果.为了验证本文方法的有效性,对3幅纹理图像和4幅SAR图像进行分割实验,并对比K-means方法、基于NyStrom逼近的谱聚类方法、Meta-clustering方法,本文的方法无论是定性还是定量分析都是较好的,并具有一定的实用性.
Shifted Non-negative Matrix Factorization
DEFF Research Database (Denmark)
Mørup, Morten; Madsen, Kristoffer Hougaard; Hansen, Lars Kai
2007-01-01
where a shift in onset of frequency profile can be induced by the Doppler effect. However, the model is also relevant for biomedical data analysis where the sources are given by compound intensities over time and the onset of the profiles have different delays to the sensors. A simple algorithm based...
Enforced Sparse Non-Negative Matrix Factorization
2016-01-23
league electrons album jewish party electron band jews war atoms albums judaism elections hydrogen israel president isotopes hebrew Figure 5. Top five...find the NMF topics sequentially by converging one topic at a time. We can do this by considering the NMF using block matrices A ≈ UV T = [U1 U2 ][V1 V2...T = U1V T1 + U2V T2 (5.8) where U1 and V1 are matrices whose column vectors consist of previously converged NMF topics, and U2 and V2 are single
Institute of Scientific and Technical Information of China (English)
李雨谦; 皮亦鸣
2011-01-01
Change detection has attracted much attention for the application of disaster monitoring. Multi-band SPOT remote sensing images are wildly used because of the abundant spectrum information. But the data redundancy needs to be eliminated using image fusion technique, which has developed rapidly. Non-negative matrix factorization (NMF) has been proven to be a very effective image fusion tool to extract useful information. In this paper, a time-sharing fusion method based on NMF is proposed for the purpose of change detection. First, the 3 band SPOT images of the same time were fused using NMF algorithm for different time period, respectively. Then the residual image, which was generated using the fused images of different time, was used to indicate the changed area. The results are able to present changed area clearly and show better performance compared to the traditional methods. It demonstrates that first using image fusing for images of different time period is able to exclude redundancy and keep useful information which helps to detect the changed area with higher accuracy.%SPOT遥感图像多光谱波段信息丰富,在土地覆盖、环境变化等诸多领域中得到广泛应用.图像融合近几年来成为学术界研究的热点,可以有效去除多光谱图像中的冗余,保留有用信息.对不同时段多光谱图像的融合进行地物变化检测,在灾害监测工作中具有重要的应用价值.文章针对多波段SPOT图像,利用基于非负矩阵分解的分时融合方法,对不同时段SPOT多波段图像进行融合,通过构造差值影像对变化区域进行检测.利用本文方法得到的图像可以清晰地表示出目标的变化区域,且正确率较高.结果表明,首先利用非负矩阵分解对不同时段图像进行融合,可以分别得到更为准确的融合图像,从而提高变化检测结果的精度.实验结果与传统方法进行了分析对比,证明了该方法的有效性.
Energy Technology Data Exchange (ETDEWEB)
2016-07-19
This code is a toy (short) version of CODE-2016-83. From a general perspective, the code represents an unsupervised adaptive machine learning algorithm that allows efficient and high performance de-mixing and feature extraction of a multitude of non-negative signals mixed and recorded by a network of uncorrelated sensor arrays. The code identifies the number of the mixed original signals and their locations. Further, the code also allows deciphering of signals that have been delayed in regards to the mixing process in each sensor. This code is high customizable and it can be efficiently used for a fast macro-analyses of data. The code is applicable to a plethora of distinct problems: chemical decomposition, pressure transient decomposition, unknown sources/signal allocation, EM signal decomposition. An additional procedure for allocation of the unknown sources is incorporated in the code.
Convergence analysis of a Pad\\'{e} family of iterations for the matrix sector function
Karp, Dmitry B
2011-01-01
The main purpose of this paper is to give a solution to a conjecture concerning a Pad\\'{e} family of iterations for the matrix sector function that was recently raised by B. Laszkiewicz et al in [A Pad\\'{e} family of iterations for the matrix sector function and the matrix $p$th root, Numer. Linear Algebra Appl. 2009; 16:951-970]. Using a sharpened version Schwarz's lemma, we also demonstrate a strengthening of the conjecture.
Coefficient Conditions for Starlikeness of Nonnegative Order
Directory of Open Access Journals (Sweden)
Rosihan M. Ali
2012-01-01
Full Text Available Sufficient conditions on a sequence {ak} of nonnegative numbers are obtained that ensures f(z=∑k=1∞akzk is starlike of nonnegative order in the unit disk. A result of Vietoris on trigonometric sums is extended in this pursuit. Conditions for close to convexity and convexity in the direction of the imaginary axis are also established. These results are applied to investigate the starlikeness of functions involving the Gaussian hypergeometric functions.
Family-Centered Early Intervention Visual Impairment Services through Matrix Session Planning
Ely, Mindy S.; Gullifor, Kateri; Hollinshead, Tara
2017-01-01
Early intervention visual impairment services are built on a model that values family. Matrix session planning pulls together parent priorities, family routines, and identified strategies in a way that helps families and early intervention professionals outline a plan that can both highlight long-term goals and focus on what can be done today.…
Hyperorthogonal family of vectors and the associated Gram matrix
DEFF Research Database (Denmark)
Fuglede, Bent
2014-01-01
A family of non-zero vectors in Euclidean n -space is termed hyperorthogonal if the angle between any two distinct vectors of the family is at least π/2 . Any hyperorthogonal family is finite and contains at most 2n vectors. It decomposes uniquely into the union of mutually orthogonal irreducible...
When to call a linear system nonnegative
Nieuwenhuis, J.W.
1998-01-01
In this paper we will consider discrete time invariant linear systems that allow for an input-state-output representation with a finite dimensional state space, and that have a finite number of inputs and outputs. The basic issue in this paper is when to call these systems nonnegative. An important
The CKM matrix from anti-SU(7) unification of GUT families
Kim, Jihn E; Seo, Min-Seok
2015-01-01
We estimate the CKM matrix elements in the recently proposed minimal model, anti-SU(7) GUT for the family unification, $[\\,3\\,]+2\\,[\\,2\\,]+8\\,[\\,\\bar{1}\\,]$+\\,(singlets). It is shown that the real angles of the right-handed unitary matrix diagonalizing the mass matrix can be determined to fit the Particle Data Group data. However, the phase in the right-handed unitary matrix is not constrained very much. We also includes an argument about allocating the Jarlskog phase in the CKM matrix. Phenomenologically, there are three classes of possible parametrizations, $\\delq=\\alpha,\\beta,$ or $\\gamma$ of the unitarity triangle. For the choice of $\\delq=\\alpha$, the phase is close to a maximal one.
2001-01-01
Enterasys Networks Inc., today announced its new Matrix E1 family of 10-Gigabit and Gigabit Ethernet switches. The Matrix E1 Optical Access Switch (OAS) enables organizations to deliver applications at 10-Gb speeds across a single fibre optic pair. Jacques Altaber, deputy leader of IT at CERN said "High-bandwith solutions are essential to leveraging more computing power, so 10-Gb Ethernet is the next logical step for us...The Matrix E1 allows us to provide the networking support that our scientists need and gives us a certain future for bandwidth and computing expansion".
Generalized Synchronization of Different Chaotic Systems Based on Nonnegative Off-Diagonal Structure
Directory of Open Access Journals (Sweden)
Ling Guo
2013-01-01
Full Text Available The generalized synchronization problem is studied in this paper for different chaotic systems with the aid of the direct design method. Based on Lyapunov stability theory and matrix theory, some sufficient conditions guaranteeing the stability of a nonlinear system with nonnegative off-diagonal structure are obtained. Then the control scheme is designed from the stable system by the direct design method. Finally, two numerical simulations are provided to verify the effectiveness and feasibility of the proposed method.
The CKM matrix from anti-SU(7 unification of GUT families
Directory of Open Access Journals (Sweden)
Jihn E. Kim
2015-10-01
Full Text Available We estimate the CKM matrix elements in the recently proposed minimal model, anti-SU(7 GUT for the family unification, [3]+2[2]+8[1¯]+(singlets. It is shown that the real angles of the right-handed unitary matrix diagonalizing the mass matrix can be determined to fit the Particle Data Group data. However, the phase in the right-handed unitary matrix is not constrained very much. At present, there are three classes of possible CKM parametrizations, δCKM=α,β, or γ of the unitarity triangle. For the choice of δCKM=α, it is easy to show that the phase is close to a maximal one, which has a parametrization-independent meaning.
The CKM matrix from anti-SU(7) unification of GUT families
Kim, Jihn E.; Mo, Doh Young; Seo, Min-Seok
2015-10-01
We estimate the CKM matrix elements in the recently proposed minimal model, anti-SU(7) GUT for the family unification, [ 3 ] + 2 [ 2 ] + 8 [ 1 bar ] +(singlets). It is shown that the real angles of the right-handed unitary matrix diagonalizing the mass matrix can be determined to fit the Particle Data Group data. However, the phase in the right-handed unitary matrix is not constrained very much. At present, there are three classes of possible CKM parametrizations, δCKM = α , β, or γ of the unitarity triangle. For the choice of δCKM = α, it is easy to show that the phase is close to a maximal one, which has a parametrization-independent meaning.
Quark and Lepton Mass Matrix Model with Only Six Family-Independent Parameters
Koide, Yoshio
2015-01-01
We propose a unified mass matrix model for quarks and leptons, in which sixteen observables of mass ratios and mixings of the quarks and neutrinos are described by using no family number-dependent parameters except for the charged lepton masses and only six family number-independent free parameters. The model is constructed by extending the so-called ``Yukawaon" model to a seesaw type model with the smallest number of possible family number-independent free parameters. As a result, once the six parameters is fixed by the quark mixing and the mass ratios of quarks and neutrinos, no free parameters are left in the lepton mixing matrix. The results are in excellent agreement with the neutrino mixing data. We predict $\\delta_{CP}^\\ell =-68^\\circ$ for the leptonic $CP$ violating phase and $\\langle m\\rangle\\simeq 21$ meV for the effective Majorana neutrino mass.
Teaching Tip: When a Matrix and Its Inverse Are Stochastic
Ding, J.; Rhee, N. H.
2013-01-01
A stochastic matrix is a square matrix with nonnegative entries and row sums 1. The simplest example is a permutation matrix, whose rows permute the rows of an identity matrix. A permutation matrix and its inverse are both stochastic. We prove the converse, that is, if a matrix and its inverse are both stochastic, then it is a permutation matrix.
Slawski, Martin
2012-01-01
Least squares fitting is in general not useful for high-dimensional linear models, in which the number of predictors is of the same or even larger order of magnitude than the number of samples. Theory developed in recent years has coined a paradigm according to which sparsity-promoting regularization is regarded as a necessity in such setting. Deviating from this paradigm, we show that non-negativity constraints on the regression coefficients may be similarly effective as explicit regularization. For a broad range of designs with Gram matrix having non-negative entries, we establish bounds on the $\\ell_2$-prediction error of non-negative least squares (NNLS) whose form qualitatively matches corresponding results for $\\ell_1$-regularization. Under slightly stronger conditions, it is established that NNLS followed by hard thresholding performs excellently in terms of support recovery of an (approximately) sparse target, in some cases improving over $\\ell_1$-regularization. A substantial advantage of NNLS over r...
Directory of Open Access Journals (Sweden)
R. Ezzati
2014-09-01
Full Text Available We propose an approach for computing an approximate nonnegative symmetric solution of some fully fuzzy linear system of equations, where the components of the coefficient matrix and the right hand side vector are nonnegative fuzzy numbers, considering equality of the median intervals of the left and right hand sides of the system. We convert the m×n fully fuzzy linear system to two m×n real linear systems, one being related to the cores and the other being concerned with spreads of the solution. We propose an approach for solving the real systems using the modified Huang method of the Abaffy-Broyden-Spedicato (ABS class of algorithms. An appropriate constrained least squares problem is solved when the solution does not satisfy nonnegative fuzziness conditions, that is, when the obtained solution vector for the core system includes a negative component, or the solution of the spread system has at least one negative component, or there exists an index for which the component of the spread is greater than the corresponding component of the core. As a special case, we discuss fuzzy systems with the components of the coefficient matrix as real crisp numbers. We finally present two computational algorithms and illustrate their effectiveness by solving some randomly generated consistent as well as inconsistent systems.
Totally nonnegative Grassmannian and Grassmann polytopes
Lam, Thomas
2015-01-01
These are lecture notes intended to supplement my second lecture at the Current Developments in Mathematics conference in 2014. In the first half of article, we give an introduction to the totally nonnegative Grassmannian together with a survey of some more recent work. In the second half of the article, we give a definition of a Grassmann polytope motivated by work of physicists on the amplituhedron. We propose to use Schubert calculus and canonical bases to replace linear algebra and convexity in the theory of polytopes.
Institute of Scientific and Technical Information of China (English)
谈爱玲; 毕卫红; 赵勇
2011-01-01
A novel method was proposed to discriminate differem kinds of spilled oil. The identification of the spilled oils has great significance to developing the treatment program and tracking the source. The present method adapts to Fourier transform NIR spectrophotometer to collect the spectral data of simulation gasoline, diesel fuel and kerosene oil spills. The Sparse Nonnegative Matrix Factorization algorithm was used to extract features. Through training with 210 samples and 5-fold cross-validation, the authors constructed the qualitatvie analysis model based on support vector machine. The authors also researched the effect of the number of features and sparseness factor. The proposed method has the identification capabilities with the accuracy of 97.78％ for 90 samples for validation. The present method of SNMF-SVM has a good identification effect and strong generalization ability,and can work as a new method for rapid identification of spilled oil%提出一种海洋溢油近红外光谱特征提取与种类鉴别新方法.海面溢油种类鉴别对现场应急处置方案的制定和可疑溢油源的追踪具有重要意义.采用傅里叶变换近红外光谱仪测定汽油、柴油、煤油三类模拟海洋溢油样本的近红外光谱,基于稀疏非负矩阵分解算法对光谱进行特征提取,采用五重交义检验,对210个样本进行训练,建立基于支持向量机的溢油光谱定性分析模型,同时讨论非负特征基数目以及稀疏因子对分类正确率的影响;利用训练好的分类器对90个未知样本进行鉴别,识别正确率达97.78%.所提出的稀疏非负矩阵分解结合支持向量机的近红外光谱定性分析方法,识别正确率高,模型泛化能力强,具有很好的分类效果,为海洋溢油的快速鉴别提供了新途径.
Performance analysis of alpha divergence in nonnegative matrix ...
African Journals Online (AJOL)
user
The paper also looks into the performance of the algorithm as important ... DOI: http://dx.doi.org/10.4314/ijest.v3i6.22. 1. Introduction. Blind Source Separation represents a larger framework of the class of unsupervised learning algorithms used in ...... His major research interests are Signal Processing, Machine Learning and.
How quantum are non-negative wavefunctions?
Energy Technology Data Exchange (ETDEWEB)
Hastings, M. B. [Station Q, Microsoft Research, Santa Barbara, California 93106-6105, USA and Quantum Architectures and Computation Group, Microsoft Research, Redmond, Washington 98052 (United States)
2016-01-15
We consider wavefunctions which are non-negative in some tensor product basis. We study what possible teleportation can occur in such wavefunctions, giving a complete answer in some cases (when one system is a qubit) and partial answers elsewhere. We use this to show that a one-dimensional wavefunction which is non-negative and has zero correlation length can be written in a “coherent Gibbs state” form, as explained later. We conjecture that such holds in higher dimensions. Additionally, some results are provided on possible teleportation in general wavefunctions, explaining how Schmidt coefficients before measurement limit the possible Schmidt coefficients after measurement, and on the absence of a “generalized area law” [D. Aharonov et al., in Proceedings of Foundations of Computer Science (FOCS) (IEEE, 2014), p. 246; e-print arXiv.org:1410.0951] even for Hamiltonians with no sign problem. One of the motivations for this work is an attempt to prove a conjecture about ground state wavefunctions which have an “intrinsic” sign problem that cannot be removed by any quantum circuit. We show a weaker version of this, showing that the sign problem is intrinsic for commuting Hamiltonians in the same phase as the double semion model under the technical assumption that TQO-2 holds [S. Bravyi et al., J. Math. Phys. 51, 093512 (2010)].
On Nonnegative Signed Domination in Graphs and its Algorithmic Complexity
Directory of Open Access Journals (Sweden)
Zhongsheng Huang
2013-02-01
Full Text Available Let G = (V, E be a simple graph with vertex set V and edge set E. A function f from V to a set {-1, 1} is said to be a nonnegative signed dominating function (NNSDF if the sum of its function values over any closed neighborhood is at least zero. The weight of f is the sum of function values of vertices in V. The nonnegative signed domination number for a graph G equals the minimum weight of a nonnegative signed dominating function of G. In this paper, exact values are found for cycles, stars, wheels, spiders and complete equally bipartite graphs and we present some lower bounds for nonnegative signed domination number in terms of the order and the maximum and minimum degree. Fothermore, we show that the decision problem corresponding to the problem of computing the nonnegative signed domination number is NP-complete.
On some properties of nonnegative weakly irreducible tensors
Yang, Yuning
2011-01-01
In this paper, we mainly focus on how to generalize some conclusions from \\emph{nonnegative irreducible tensors} to \\emph{nonnegative weakly irreducible tensors}. To do so, a basic lemma as Lemma 3.1 of \\cite{s11} is proven using new tools. First, we define the stochastic tensor. Then we show that every nonnegative weakly irreducible tensor with spectral radius be 1 is diagonally similar to a unique weakly irreducible stochastic tensor. Based on it, we prove some important lemmas, which help us to generalize the results.
The matrix supporter’s work in the family health strategy
Directory of Open Access Journals (Sweden)
Anne Jaquelyne Roque Barrêto
2012-04-01
Full Text Available The objective of this study was to recognize the working process of the matrix supporters who work in the Family Health Strategy. It is a qualitative research, developed in a Sanitary District, located in one of the towns of the metropolitan region of João Pessoa – PB. It was observed that the matrix supporters is considered an articulator and facilitator of the process of work of the team who is opposite to the management. He develops activities which are managerial from the assessment of the process of work of each sector of the service and encourages the development of spaces of co-management. Another activity related to the assistential dimension which is guided by the proposal of the expanded health clinics. We so conclude that, the work of the matrix supporters configures a strategy of management that corroborates with the strength of the building up of an integral and humanized care according to what advocates the SUS (Brazilian Government Health System.
Trends in global warming and evolution of matrix protein 2 family from influenza A virus.
Yan, Shao-Min; Wu, Guang
2009-12-01
The global warming is an important factor affecting the biological evolution, and the influenza is an important disease that threatens humans with possible epidemics or pandemics. In this study, we attempted to analyze the trends in global warming and evolution of matrix protein 2 family from influenza A virus, because this protein is a target of anti-flu drug, and its mutation would have significant effect on the resistance to anti-flu drugs. The evolution of matrix protein 2 of influenza A virus from 1959 to 2008 was defined using the unpredictable portion of amino-acid pair predictability. Then the trend in this evolution was compared with the trend in the global temperature, the temperature in north and south hemispheres, and the temperature in influenza A virus sampling site, and species carrying influenza A virus. The results showed the similar trends in global warming and in evolution of M2 proteins although we could not correlate them at this stage of study. The study suggested the potential impact of global warming on the evolution of proteins from influenza A virus.
Shimada, Nao; Nishio, Keiko; Maeda, Mineko; Urushihara, Hideko; Kawata, Takefumi
2004-10-01
Dd-STATa is a functional Dictyostelium homologue of metazoan STAT (signal transducers and activators of transcription) proteins, which is activated by cAMP and is thereby translocated into the nuclei of anterior tip cells of the prestalk region of the slug. By using in situ hybridization analyses, we found that the SLF308 cDNA clone, which contains the ecmF gene that encodes a putative extracellular matrix protein and is expressed in the anterior tip cells, was greatly down-regulated in the Dd-STATa-null mutant. Disruption of the ecmF gene, however, resulted in almost no phenotypic change. The absence of any obvious mutant phenotype in the ecmF-null mutant could be due to a redundancy of similar genes. In fact, a search of the Dictyostelium whole genome database demonstrates the existence of an additional 16 homologues, all of which contain a cellulose-binding module. Among these homologues, four genes show Dd-STATa-dependent expression, while the others are Dd-STATa-independent. We discuss the potential role of Dd-STATa in morphogenesis via its effect on the interaction between cellulose and these extracellular matrix family proteins.
Non-negative constraint for image-based breathing gating in ultrasound hepatic perfusion data
Wu, Kaizhi; Ding, Mingyue; Chen, Xi; Deng, Wenjie; Zhang, Zhijun
2015-12-01
Images acquired during free breathing using contrast enhanced ultrasound hepatic perfusion imaging exhibits a periodic motion pattern. It needs to be compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. To reduce the impact of respiratory motion, image-based breathing gating algorithm was used to compensate the respiratory motion in contrast enhanced ultrasound. The algorithm contains three steps of which respiratory kinetics extracted, image subsequences determined and image subsequences registered. The basic performance of the algorithm was to extract the respiratory kinetics of the ultrasound hepatic perfusion image sequences accurately. In this paper, we treated the kinetics extracted model as a non-negative matrix factorization (NMF) problem. We extracted the respiratory kinetics of the ultrasound hepatic perfusion image sequences by non-negative matrix factorization (NMF). The technique involves using the NMF objective function to accurately extract respiratory kinetics. It was tested on simulative phantom and used to analyze 6 liver CEUS hepatic perfusion image sequences. The experimental results show the effectiveness of our proposed method in quantitative and qualitative.
Families of Artin-Schreier curves with Cartier-Manin matrix of constant rank
Farnell, Shawn
2012-01-01
Let k be an algebraically closed field of characteristic p > 0. Every Artin-Schreier k-curve X has an equation of the form y^p - y = f(x) for some f(x) in k(x) such that p does not divide the least common multiple L of the orders of the poles of f(x). Under the condition that p is congruent to 1 mod L, Zhu proved that the Newton polygon of the L-function of X is determined by the Hodge polygon of f(x). In particular, the Newton polygon depends only on the orders of the poles of f(x) and not on the location of the poles or otherwise on the coefficients of f(x). In this paper, we prove an analogous result about the a-number of the p-torsion group scheme of the Jacobian of X, providing the first non-trivial examples of families of Jacobians with constant a-number. Equivalently, we consider the semi-linear Cartier operator on the sheaf of regular 1-forms of X and provide the first non-trivial examples of families of curves whose Cartier-Manin matrix has constant rank.
Multivariate Matrix-Exponential Distributions
DEFF Research Database (Denmark)
Bladt, Mogens; Nielsen, Bo Friis
2010-01-01
-exponential distributions. We prove a characterization that states that a distribution is an MVME distribution if and only if all non-negative, non-null linear combinations of the coordinates have a univariate matrix-exponential distribution. This theorem is analog to a well-known characterization theorem...
1988-06-30
MATRICES . The monograph Nonnegative Matrices [6] is an advanced book on all aspect of the theory of nonnegative matrices and...and on inverse eigenvalue problems for nonnegative matrices . The work explores some of the most recent developments in the theory of nonnegative...k -1, t0 . Define the associated polynomial of type <z>: t t-t 2 t-t 3 t-tk_ 1,X - x - x . . .X- where t = tk . The
Nonnegative spline regression of incomplete tracing data reveals high resolution neural connectivity
Harris, Kameron Decker; Shea-Brown, Eric
2016-01-01
Whole-brain neural connectivity data are now available from viral tracing experiments, which reveal the connections between a source injection site and elsewhere in the brain. These hold the promise of revealing spatial patterns of connectivity throughout the mammalian brain. To achieve this goal, we seek to fit a weighted, nonnegative adjacency matrix among 100 {\\mu}m brain "voxels" using viral tracer data. Despite a multi-year experimental effort, the problem remains severely underdetermined: Injection sites provide incomplete coverage, and the number of voxels is orders of magnitude larger than the number of injections. Furthermore, projection data are missing within the injection site because local connections there are not separable from the injection signal. We use a novel machine-learning algorithm to meet these challenges and develop a spatially explicit, voxel-scale connectivity map of the mouse visual system. Our method combines three features: a matrix completion loss for missing data, a smoothing ...
Exploratory matrix factorization for PET image analysis.
Kodewitz, A; Keck, I R; Tomé, A M; Lang, E W
2010-01-01
Features are extracted from PET images employing exploratory matrix factorization techniques such as nonnegative matrix factorization (NMF). Appropriate features are fed into classifiers such as a support vector machine or a random forest tree classifier. An automatic feature extraction and classification is achieved with high classification rate which is robust and reliable and can help in an early diagnosis of Alzheimer's disease.
Matrix subordinators and related Upsilon transformations
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Pérez-Abreu, V.
2008-01-01
A class of upsilon transformations of Lévy measures for matrix subordinators is introduced. Some regularizing properties of these transformations are derived, such as absolute continuity and complete monotonicity. The class of Lévy measures with completely monotone matrix densities is characterized....... Examples of infinitely divisible nonnegative definite random matrices are constructed using an upsilon transformation....
NON-NEGATIVE RADIAL SOLUTION FOR AN ELLIPTIC EQUATION
Institute of Scientific and Technical Information of China (English)
Yang Guoying; Guo Zongming
2005-01-01
We study the structure and behavior of non-negative radial solution for the following elliptic equation △u = uv, x ∈ Rn with 0 ＜ v ＜ 1. We also obtain the detailed asymptotic expansion of u near infinity.
Nonpolytopal nonsimplicial lattice spheres with nonnegative toric g-vector
Billera, Louis J
2011-01-01
We construct many nonpolytopal nonsimplicial Gorenstein* meet semi-lattices with nonnegative toric g-vector, supporting a conjecture of Stanley. These are formed as Bier spheres over the face posets of multiplexes, polytopes constructed by Bisztriczky as generalizations of simplices.
Automorphisms of semigroups of invertible matrices with nonnegative integer elements
Energy Technology Data Exchange (ETDEWEB)
Semenov, Pavel P [M. V. Lomonosov Moscow State University, Faculty of Mechanics and Mathematics, Moscow (Russian Federation)
2012-09-30
Let G{sub n}(Z) be the subsemigroup of GL{sub n}(Z) consisting of the matrices with nonnegative integer coefficients. In the paper, the automorphisms of this semigroup are described for n{>=}2. Bibliography: 5 titles.
Algorithms for Sparse Non-negative Tucker Decompositions
DEFF Research Database (Denmark)
Mørup, Morten; Hansen, Lars Kai
2008-01-01
for Tucker decompositions when indeed the data and interactions can be considered non-negative. We further illustrate how sparse coding can help identify what model (PARAFAC or Tucker) is the most appropriate for the data as well as to select the number of components by turning off excess components...
Joint cluster and non-negative least squares analysis for aerosol mass spectrum data
Energy Technology Data Exchange (ETDEWEB)
Zhang, T; Zhu, W [Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600 (United States); McGraw, R [Environmental Sciences Department, Brookhaven National Laboratory, Upton, NY 11973-5000 (United States)], E-mail: zhu@ams.sunysb.edu
2008-07-15
Aerosol mass spectrum (AMS) data contain hundreds of mass to charge ratios and their corresponding intensities from air collected through the mass spectrometer. The observations are usually taken sequentially in time to monitor the air composition, quality and temporal change in an area of interest. An important goal of AMS data analysis is to reduce the dimensionality of the original data yielding a small set of representing tracers for various atmospheric and climatic models. In this work, we present an approach to jointly apply the cluster analysis and the non-negative least squares method towards this goal. Application to a relevant study demonstrates the effectiveness of this new approach. Comparisons are made to other relevant multivariate statistical techniques including the principal component analysis and the positive matrix factorization method, and guidelines are provided.
Algorithms for Sparse Non-negative Tucker Decompositions
DEFF Research Database (Denmark)
Mørup, Morten; Hansen, Lars Kai
2008-01-01
There is a increasing interest in analysis of large scale multi-way data. The concept of multi-way data refers to arrays of data with more than two dimensions, i.e., taking the form of tensors. To analyze such data, decomposition techniques are widely used. The two most common decompositions...... decompositions). To reduce ambiguities of this type of decomposition we develop updates that can impose sparseness in any combination of modalities, hence, proposed algorithms for sparse non-negative Tucker decompositions (SN-TUCKER). We demonstrate how the proposed algorithms are superior to existing algorithms...... for Tucker decompositions when indeed the data and interactions can be considered non-negative. We further illustrate how sparse coding can help identify what model (PARAFAC or Tucker) is the most appropriate for the data as well as to select the number of components by turning off excess components...
Parallel Nonnegative Least Squares Solvers for Model Order Reduction
2016-03-01
not for the PQN method. For the latter method the size of the active set is controlled to promote sparse solutions. This is described in Section 3.2.1...or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington...21005-5066 primary author’s email: <james.p.collins106.civ@mail.mil>. Parallel nonnegative least squares (NNLS) solvers are developed specifically for
Convergence, Non-negativity and Stability of a New Milstein Scheme with Applications to Finance
Higham, Desmond J; Szpruch, Lukasz
2012-01-01
We propose and analyse a new Milstein type scheme for simulating stochastic differential equations (SDEs) with highly nonlinear coefficients. Our work is motivated by the need to justify multi-level Monte Carlo simulations for mean-reverting financial models with polynomial growth in the diffusion term. We introduce a double implicit Milstein scheme and show that it possesses desirable properties. It converges strongly and preserves non-negativity for a rich family of financial models and can reproduce linear and nonlinear stability behaviour of the underlying SDE without severe restriction on the time step. Although the scheme is implicit, we point out examples of financial models where an explicit formula for the solution to the scheme can be found.
DEFF Research Database (Denmark)
Volck, B; Price, P A; Johansen, J S;
1998-01-01
YKL-40, also called human cartilage glycoprotein-39 (HC gp-39), is a member of family 18 glycosyl hydrolases. YKL-40 is secreted by chondrocytes, synovial cells, and macrophages, and recently it has been reported that YKL-40 has a role as an autoantigen in rheumatoid arthritis (RA). The function...... of YKL-40 is unknown, but the pattern of its expression in normal and disease states suggests that it could function in remodeling or degradation of the extracellular matrix. High levels of YKL-40 are found in synovial fluid from patients with active RA. Neutrophils are abundant in synovial fluid...
Linear Program Relaxation of Sparse Nonnegative Recovery in Compressive Sensing Microarrays
Directory of Open Access Journals (Sweden)
Linxia Qin
2012-01-01
Full Text Available Compressive sensing microarrays (CSM are DNA-based sensors that operate using group testing and compressive sensing principles. Mathematically, one can cast the CSM as sparse nonnegative recovery (SNR which is to find the sparsest solutions subjected to an underdetermined system of linear equations and nonnegative restriction. In this paper, we discuss the l1 relaxation of the SNR. By defining nonnegative restricted isometry/orthogonality constants, we give a nonnegative restricted property condition which guarantees that the SNR and the l1 relaxation share the common unique solution. Besides, we show that any solution to the SNR must be one of the extreme points of the underlying feasible set.
Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays.
Qin, Linxia; Xiu, Naihua; Kong, Lingchen; Li, Yu
2012-01-01
Compressive sensing microarrays (CSM) are DNA-based sensors that operate using group testing and compressive sensing principles. Mathematically, one can cast the CSM as sparse nonnegative recovery (SNR) which is to find the sparsest solutions subjected to an underdetermined system of linear equations and nonnegative restriction. In this paper, we discuss the l₁ relaxation of the SNR. By defining nonnegative restricted isometry/orthogonality constants, we give a nonnegative restricted property condition which guarantees that the SNR and the l₁ relaxation share the common unique solution. Besides, we show that any solution to the SNR must be one of the extreme points of the underlying feasible set.
Institute of Scientific and Technical Information of China (English)
LIU Feng-xia; LI Yan-xiang; ZHANG Xu-de; REN Cui-ai; HUANG Shang-zhi; YU Meng-xue
2013-01-01
Background Multiple epiphysis dysplasia (MED) is a common skeletal dysplasia with a significant locus heterogeneity.In the majority of clinically defined.cases,mutations have been identified in the gene encoding cartilage algometric matrix protein (COMP).Methods Five patients were included in the study.Linkage analysis and mutation analysis of the COMP gene were conducted in the patients and their family members.Results We have identified a novel mutation in axon 14 of COMPgene in the family.Conclusions This mutation produced a severe MED phenotype with marked short stature,early onset osteoarthritis,and remarkable radiographic changes.Our results extended the range of disease-causing mutations in COMP gene and contributed more information about relationship between mutations and phenotype.
非负整数对称阵可实现性问题的算法%ON THE REALIZABILITY PROBLEM OF NONNEGATIVE INTEGRAL SYMMETRIC MATRICES
Institute of Scientific and Technical Information of China (English)
孙峰; 王学平
2011-01-01
In this paper, we show several necessary and surfficient conditions for the realizability of n-order nonnegative integral symmetric matrices, convert their realizability problems into the determination of nonnegative integral solutions to equations or inequalities. Based on finding a Hilbert basis for the set of nonnegative integral solutions of a system of linear equations or inequalities with integral coefficients, we obtain three algorithms which not only determine whether a matrix is realizabe but also give one of its realization matrix and content when it is realizable.%1引 言 设P是有p个元素,oj,j＝1,…,p,的有限集,{Si｝,I=1,…,n,为P的子集族.记A=(aij)为{Si｝的关联矩阵,其中,当Oj∈Si时aij=1,否则aij=0.若AAT＝B＝(bij),即bij=|Si ∩ Sj|,则B是对称的且bii≥Bij≥0.反过来,已知n阶非负整数对称阵B,是否存在一个n×m的0-1矩阵A使B=AAT,以及如何计算使B=AAT成立的最小的m(即容度),这即是John B Kelly于1968年在文献[1]中讨论的非负整数对称阵的可实现性问题.
Directory of Open Access Journals (Sweden)
Christine F Skibola
Full Text Available BACKGROUND: Non-Hodgkin lymphoma (NHL is the fifth most common cancer in the U.S. and few causes have been identified. Genetic association studies may help identify environmental risk factors and enhance our understanding of disease mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: 768 coding and haplotype tagging SNPs in 146 genes were examined using Illumina GoldenGate technology in a large population-based case-control study of NHL in the San Francisco Bay Area (1,292 cases 1,375 controls are included here. Statistical analyses were restricted to HIV- participants of white non-Hispanic origin. Genes involved in steroidogenesis, immune function, cell signaling, sunlight exposure, xenobiotic metabolism/oxidative stress, energy balance, and uptake and metabolism of cholesterol, folate and vitamin C were investigated. Sixteen SNPs in eight pathways and nine haplotypes were associated with NHL after correction for multiple testing at the adjusted q<0.10 level. Eight SNPs were tested in an independent case-control study of lymphoma in Germany (494 NHL cases and 494 matched controls. Novel associations with common variants in estrogen receptor 1 (ESR1 and in the vitamin C receptor and matrix metalloproteinase gene families were observed. Four ESR1 SNPs were associated with follicular lymphoma (FL in the U.S. study, with rs3020314 remaining associated with reduced risk of FL after multiple testing adjustments [odds ratio (OR = 0.42, 95% confidence interval (CI = 0.23-0.77 and replication in the German study (OR = 0.24, 95% CI = 0.06-0.94. Several SNPs and haplotypes in the matrix metalloproteinase-3 (MMP3 and MMP9 genes and in the vitamin C receptor genes, solute carrier family 23 member 1 (SLC23A1 and SLC23A2, showed associations with NHL risk. CONCLUSIONS/SIGNIFICANCE: Our findings suggest a role for estrogen, vitamin C and matrix metalloproteinases in the pathogenesis of NHL that will require further validation.
Wind Noise Reduction using Non-negative Sparse Coding
DEFF Research Database (Denmark)
Schmidt, Mikkel N.; Larsen, Jan; Hsiao, Fu-Tien
2007-01-01
We introduce a new speaker independent method for reducing wind noise in single-channel recordings of noisy speech. The method is based on non-negative sparse coding and relies on a wind noise dictionary which is estimated from an isolated noise recording. We estimate the parameters of the model...... and discuss their sensitivity. We then compare the algorithm with the classical spectral subtraction method and the Qualcomm-ICSI-OGI noise reduction method. We optimize the sound quality in terms of signal-to-noise ratio and provide results on a noisy speech recognition task....
Unidimensional nonnegative scaling for genome-wide linkage disequilibrium maps.
Liao, Haiyong; Ng, Michael; Fung, Eric; Sham, Pak C
2008-01-01
The main aim of this paper is to propose and develop a unidimensional nonnegative scaling model to construct Linkage Disequilibrium (LD) maps. The proposed constrained scaling model can be efficiently solved by transforming it to an unconstrained model. The method is implemented in PC Clusters at Hong Kong Baptist University. The LD maps are constructed for four populations from Hapmap data sets with chromosomes of several ten thousand Single Nucleotide Polymorphisms (SNPs). The similarities and dissimilarities of the LD maps are studied and analysed. Computational results are also reported to show the effectiveness of the method using parallel computation.
Representations of non-negative polynomials via critical ideals
Hiep, Dang Tuan
2011-01-01
This paper studies the representations of a non-negative polynomial $f$ on a non-compact semi-algebraic set $K$ modulo its critical ideal. Under the assumptions that the semi-algebraic set $K$ is regular and $f$ satisfies the boundary Hessian conditions (BHC) at each zero of $f$ in $K$, we show that $f$ can be represented as a sum of squares (SOS) of real polynomials modulo its critical ideal if $f\\ge 0$ on $K$. In particular, we focus on the polynomial ring $\\mathbb R[x]$.
Kudashev, Vadim R.; Suleimanov, Bulat I.
1998-01-01
We construct a one-parametric family of the double-scaling limits in the hermitian matrix model $\\Phi^6$ for 2D quantum gravity. The known limit of Bresin, Marinari and Parisi belongs to this family. The family is represented by the Gurevich-Pitaevskii solution of the Korteveg-de Vries equation which describes the onset of nondissipative shock waves in media with small dispersion. Numerical simulation of the universal Gurevich-Pitaevskii solution is made.
Kudashev, Vadim R.; Suleimanov, Bulat I.
1998-01-01
We construct a one-parametric family of the double-scaling limits in the hermitian matrix model $\\Phi^6$ for 2D quantum gravity. The known limit of Bresin, Marinari and Parisi belongs to this family. The family is represented by the Gurevich-Pitaevskii solution of the Korteveg-de Vries equation which describes the onset of nondissipative shock waves in media with small dispersion. Numerical simulation of the universal Gurevich-Pitaevskii solution is made.
How to project onto the monotone nonnegative cone using Pool Adjacent Violators type algorithms
Németh, A B
2012-01-01
The metric projection onto an order nonnegative cone from the metric projection onto the corresponding order cone is derived. Particularly, we can use Pool Adjacent Violators-type algorithms developed for projecting onto the monotone cone for projecting onto the monotone nonnegative cone too.
Generalized CP and $\\Delta (3n^2)$ Family Symmetry for Semi-Direct Predictions of the PMNS Matrix
Ding, Gui-Jun
2016-01-01
The generalized CP transformations can only be consistently defined in the context of $\\Delta(3n^2)$ lepton symmetry if a certain subset of irreducible representations are present in a model. We perform a comprehensive analysis of the possible automorphisms and the corresponding CP transformations of the $\\Delta(3n^2)$ group. It is sufficient to only consider three automorphisms if $n$ is not divisible by 3 while additional eight types of CP transformations could be imposed for the case of $n$ divisible by 3. We study the lepton mixing patterns which can be derived from the $\\Delta(3n^2)$ family symmetry and generalized CP in the semi-direct approach. The PMNS matrix is determined to be the trimaximal pattern for all the possible CP transformations, and it can only take two distinct forms.
An Effective Hybrid Artificial Bee Colony Algorithm for Nonnegative Linear Least Squares Problems
Directory of Open Access Journals (Sweden)
Xiangyu Kong
2014-07-01
Full Text Available An effective hybrid artificial bee colony algorithm is proposed in this paper for nonnegative linear least squares problems. To further improve the performance of algorithm, orthogonal initialization method is employed to generate the initial swarm. Furthermore, to balance the exploration and exploitation abilities, a new search mechanism is designed. The performance of this algorithm is verified by using 27 benchmark functions and 5 nonnegative linear least squares test problems. And the comparison analyses are given between the proposed algorithm and other swarm intelligence algorithms. Numerical results demonstrate that the proposed algorithm displays a high performance compared with other algorithms for global optimization problems and nonnegative linear least squares problems.
Gauvin, Laetitia; Cattuto, Ciro
2014-01-01
The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computationa...
2008-01-01
GALNT3, a gene associated with Hyperphosphatemic Familial Tumoral Calcinosis, is transcriptionally regulated by extracellular phosphate and modulates matrix metalloproteinase activity correspondence: Corresponding author. Laboratory of Molecular Dermatology Department of Dermatology Rambam Medical Center POB 9602, Haifa 31096, Israel. Tel.: +972 4 8541919; fax: +972 4 8542951. (Sprecher, Eli) (Sprecher, Eli) Labo...
Monte Carlo Algorithm for Least Dependent Non-Negative Mixture Decomposition
Astakhov, S A; Kraskov, A; Grassberger, P; Astakhov, Sergey A.; St\\"ogbauer, Harald; Kraskov, Alexander; Grassberger, Peter
2006-01-01
We propose a simulated annealing algorithm (called SNICA for "stochastic non-negative independent component analysis") for blind decomposition of linear mixtures of non-negative sources with non-negative coefficients. The de-mixing is based on a Metropolis type Monte Carlo search for least dependent components, with the mutual information between recovered components as a cost function and their non-negativity as a hard constraint. Elementary moves are shears in two-dimensional subspaces and rotations in three-dimensional subspaces. The algorithm is geared at decomposing signals whose probability densities peak at zero, the case typical in analytical spectroscopy and multivariate curve resolution. The decomposition performance on large samples of synthetic mixtures and experimental data is much better than that of traditional blind source separation methods based on principal component analysis (MILCA, FastICA, RADICAL) and chemometrics techniques (SIMPLISMA, ALS, BTEM) The source codes of SNICA, MILCA and th...
Matioc, Bogdan-Vasile
2011-01-01
We prove global existence of nonnegative weak solutions for a strongly coupled, fourth order degenerate parabolic system governing the motion of two thin fluid layers in a porous medium when capillarity is the sole driving mechanism.
Moschidis, Georgios
2016-01-01
The wave equation $\\square_{g_{M,a}}\\psi=0$ on subextremal Kerr spacetimes $(\\mathcal{M}_{M,a},g_{M,a})$, $0<|a|
Institute of Scientific and Technical Information of China (English)
吴荣玉; 樊丰; 舒建
2012-01-01
Matrix factorization is an effective tool to realize mass data processing and analysis. Non-negative matrix factorization is a kind of orthogonal transformation. It can realize non-negative decomposition in the condition that all elements are non-negative. The technology of robust Hash use the secret key to extract some robust features from multimedia content, then,these features are compressed to produce hash value. We can authenticate the authenticity of media content through comparing the Hash transited along with the media content with the Hash produced by receiver.%矩阵分解是实现大规模数据处理与分析的一种有效工具.矩阵的非负矩阵分解NMF(Non-Negative Matrix Factorization)变换是一种正交变换,是在矩阵中所有元素均为非负的条件下对其实现的非负分解.鲁棒哈希技术利用密钥提取多媒体内容的某些鲁棒特征,通过进一步压缩产生哈希值,通过比较跟随媒体内容传送来的哈希和接收端产生的哈希,实现对媒体内容的真实性认证.
Learning a Nonnegative Sparse Graph for Linear Regression.
Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung
2015-09-01
Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.
Sparse Matrix Inversion with Scaled Lasso
Sun, Tingni
2012-01-01
We propose a new method of learning a sparse nonnegative-definite target matrix. Our primary example of the target matrix is the inverse of a population covariance matrix or correlation matrix. The algorithm first estimates each column of the matrix by scaled Lasso, a joint estimation of regression coefficients and noise level, and then adjusts the matrix estimator to be symmetric. The procedure is efficient in the sense that the penalty level of the scaled Lasso for each column is completely determined by the data via convex minimization, without using cross-validation. We prove that this method guarantees the fastest proven rate of convergence in the spectrum norm under conditions of weaker form than those in the existing analyses of other $\\ell_1$ algorithms, and has faster guaranteed rate of convergence when the ratio of the $\\ell_1$ and spectrum norms of the target inverse matrix diverges to infinity. A simulation study also demonstrates the competitive performance of the proposed estimator.
Directory of Open Access Journals (Sweden)
Laetitia Gauvin
Full Text Available The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule.
Gauvin, Laetitia; Panisson, André; Cattuto, Ciro
2014-01-01
The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule.
On Convergence with respect to an Ideal and a Family of Matrices
Directory of Open Access Journals (Sweden)
Jan-David Hardtke
2014-01-01
Full Text Available P. Das et al. recently introduced and studied the notions of strong AI-summability with respect to an Orlicz function F and AI-statistical convergence, where A is a nonnegative regular matrix and I is an ideal on the set of natural numbers. In this paper, we will generalise these notions by replacing A with a family of matrices and F with a family of Orlicz functions or moduli and study the thus obtained convergence methods. We will also give an application in Banach space theory, presenting a generalisation of Simons' sup-limsup-theorem to the newly introduced convergence methods (for the case that the filter generated by the ideal I has a countable base, continuing some of the author's previous work.
ON SOLUTIONS OF MATRIX EQUATION AXAT + BYBT = C
Institute of Scientific and Technical Information of China (English)
Yuan-bei Deng; Xi-yan Hu
2005-01-01
By making use of the quotient singular value decomposition (QSVD) of a matrix pair,this paper establishes the necessary and sufficient conditions for the existence of and the expressions for the general solutions of the linear matrix equation AXAT + BYBT = C with the unknown X and Y, which may be both symmetric, skew-symmetric, nonnegative definite , positive definite or some cross combinations respectively. Also, the solutions of some optimal problems are derived.
Non-negative matrix factorization techniques advances in theory and applications
2016-01-01
This book collects new results, concepts and further developments of NMF. The open problems discussed include, e.g. in bioinformatics: NMF and its extensions applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining etc. The research results previously scattered in different scientific journals and conference proceedings are methodically collected and presented in a unified form. While readers can read the book chapters sequentially, each chapter is also self-contained. This book can be a good reference work for researchers and engineers interested in NMF, and can also be used as a handbook for students and professionals seeking to gain a better understanding of the latest applications of NMF.
BJUT at TREC 2015 Microblog Track: Real-Time Filtering Using Non-negative Matrix Factorization
2015-11-20
text and the number of followers, (4) removing the http links ,the words of length less than 3 greater than 15 and stopwords, and (5) converting the...with support vector machines: Learning with many relevant features[M]. Springer Berlin Heidelberg, 1998. [4] Albadvi A, Shahbazi M.A hybrid...Application of hybrid recommendation in web-based cooking assistant[C].Knowledge-Based Intelligent Information and Engineering Systems. Springer
Deconvolution of petroleum mixtures using mid-FTIR analysis and non-negative matrix factorization
Livanos, George; Zervakis, Michalis; Pasadakis, Nikos; Karelioti, Marouso; Giakos, George
2016-11-01
The aim of this study is to develop an efficient, robust and cost effective methodology capable of both identifying the chemical fractions in complex commercial petroleum products and numerically estimating their concentration within the mixture sample. We explore a methodology based on attenuated total reflectance fourier transform infrared (ATR-FTIR) analytical signals, combined with a modified factorization algorithm to solve this ‘mixture problem’, first in qualitative and then in quantitative mode. The proposed decomposition approach is self-adapting to data without prior knowledge and is able of accurately estimating the weight contributions of constituents in the entire chemical compound. The results of the presented work to petroleum analysis indicate that it is possible to deconvolve the mixing process and recover the content in a chemically complex petroleum mixture using the infrared signals of a limited number of samples and the principal substances forming the mixture. A focus application of the proposed methodology is the quality control of commercial gasoline by identifying and quantifying the individual fractions utilized for its formulation via a fast, robust and efficient procedure based on mathematical analysis of the acquired spectra.
Directory of Open Access Journals (Sweden)
Fei Liu
2014-01-01
Full Text Available With the development of high-throughput and low-cost sequencing technology, a large number of marine microbial sequences were generated. The association patterns between marine microbial species and environment factors are hidden in these large amount sequences. Mining these association patterns is beneficial to exploit the marine resources. However, very few marine microbial association patterns are well investigated in this field. The present study reports the development of a novel method called HC-sNMF to detect the marine microbial association patterns. The results show that the four seasonal marine microbial association networks have characters of complex networks, the same environmental factor influences different species in the four seasons, and the correlative relationships are stronger between OTUs (taxa than with environmental factors in the four seasons detecting community.
On the construction of symmetric nonnegative matrix with prescribed Ritz values
Directory of Open Access Journals (Sweden)
Alimohammad Nazari
2014-09-01
Full Text Available In this paper for a given prescribed Ritz values that satisfy inthe some special conditions, we find a symmetric nonnegativematrix, such that the given set be its Ritz values.
2015-01-20
Summarization, Proceedings of the 2014 SIAM International Conference on Data Mining . 01-MAY-14, . : , Wenting Lu, Lei Li, Tao Li, Honggang Zhang, Jun Guo. Web ...Received Paper 4.00 6.00 Wenting Lu, Jingxuan Li, Tao Li, Weidong Guo, Honggang Zhang, Jun Guo. Web Multimedia Object Classification Using Cross-Domain...Chris Ding, Jie Tang. An NMF-framework for Unifying Posterior Probabilistic Clustering and Probabilistic Latent Semantic Indexing, Communications in
Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior
DEFF Research Database (Denmark)
Arngren, Morten; Schmidt, Mikkel Nørgaard; Larsen, Jan
2011-01-01
Hyperspectral imaging can be used in assessing the quality of foods by decomposing the image into constituents such as protein, starch, and water. Observed data can be considered a mixture of underlying characteristic spectra (endmembers), and estimating the constituents and their abundances requ...... perform as good or better than existing volume constrained methods. Further, our method gives credible intervals for the endmembers and abundances, which allows us to asses the confidence of the results....
Separating inequalities for nonnegative polynomials that are not sums of squares
Iliman, Sadik
2012-01-01
Ternary sextics and quaternary quartics are the smallest cases where there exist nonnegative polynomials that are not sums of squares (SOS). A complete classification of the difference between these cones was given by G. Blekherman via analyzing the corresponding dual cones. An exact computation of the extreme rays in order to separate a fixed nonnegative polynomial that is not SOS is difficult. We provide a method substantially simplifying this computation for certain classes of polynomials on the boundary of these cones. In particular, our method yields separating extreme rays for almost every nonnegative ternary sextic with at least seven zeros. As an application to further instances, we compute a rational certificate proving that the Motzkin polynomial is not SOS.
Liang, Jian; Xie, Jun; Gao, Jing; Xu, Chao-Qun; Yan, Yi; Jia, Gan-Chu; Xiang, Liang; Xie, Li-Ping; Zhang, Rong-Qing
2016-12-01
Mantle can secret matrix proteins playing key roles in regulating the process of shell formation. The genes encoding lysine-rich matrix proteins (KRMPs) are one of the most highly expressed matrix genes in pearl oysters. However, the expression pattern of KRMPs is limited and the functions of them still remain unknown. In this study, we isolated and identified six new members of lysine-rich matrix proteins, rich in lysine, glycine and tyrosine, and all of them are basic matrix proteins. Combined with four members of the KRMPs previously reported, all these proteins can be divided into three subclasses according to the results of phylogenetic analyses: KRMP1-3 belong to subclass KPI, KRMP4-5 belong to KPII, and KRMP6-10 belong to KPIII. Three subcategories of lysine-rich matrix proteins are highly expressed in the D-phase, the larvae and adult mantle. Lysine-rich matrix proteins are involved in the shell repairing process and associated with the formation of the shell and pearl. What's more, they can cause abnormal shell growth after RNA interference. In detail, KPI subgroup was critical for the beginning formation of the prismatic layer; both KPII and KPIII subgroups participated in the formation of prismatic layer and nacreous layer. Compared with different temperatures and salinity stimulation treatments, the influence of changes in pH on KRMPs gene expression was the greatest. Recombinant KRMP7 significantly inhibited CaCO3 precipitation, changed the morphology of calcite, and inhibited the growth of aragonite in vitro. Our results are beneficial to understand the functions of the KRMP genes during shell formation.
Total coloring of graphs embedded in surfaces of nonnegative Euler characteristic
Institute of Scientific and Technical Information of China (English)
WANG HuiJuan; LIU Bin; WU JianLiang; WANG Bing
2014-01-01
Let G be a graph which can be embedded in a surface of nonnegative Euler characteristic.In this paper,it is proved that the total chromatic number of G is △（G）＋1 if △（G）9,where △（G）is the maximum degree of G.
Discrete Least-norm Approximation by Nonnegative (Trigonomtric) Polynomials and Rational Functions
Siem, A.Y.D.; de Klerk, E.; den Hertog, D.
2005-01-01
Polynomials, trigonometric polynomials, and rational functions are widely used for the discrete approximation of functions or simulation models.Often, it is known beforehand, that the underlying unknown function has certain properties, e.g. nonnegative or increasing on a certain region.However, the
Efficient non-negative constrained model-based inversion in optoacoustic tomography
Ding, Lu; Luís Deán-Ben, X.; Lutzweiler, Christian; Razansky, Daniel; Ntziachristos, Vasilis
2015-09-01
The inversion accuracy in optoacoustic tomography depends on a number of parameters, including the number of detectors employed, discrete sampling issues or imperfectness of the forward model. These parameters result in ambiguities on the reconstructed image. A common ambiguity is the appearance of negative values, which have no physical meaning since optical absorption can only be higher or equal than zero. We investigate herein algorithms that impose non-negative constraints in model-based optoacoustic inversion. Several state-of-the-art non-negative constrained algorithms are analyzed. Furthermore, an algorithm based on the conjugate gradient method is introduced in this work. We are particularly interested in investigating whether positive restrictions lead to accurate solutions or drive the appearance of errors and artifacts. It is shown that the computational performance of non-negative constrained inversion is higher for the introduced algorithm than for the other algorithms, while yielding equivalent results. The experimental performance of this inversion procedure is then tested in phantoms and small animals, showing an improvement in image quality and quantitativeness with respect to the unconstrained approach. The study performed validates the use of non-negative constraints for improving image accuracy compared to unconstrained methods, while maintaining computational efficiency.
Discrete Least-norm Approximation by Nonnegative (Trigonomtric) Polynomials and Rational Functions
Siem, A.Y.D.; de Klerk, E.; den Hertog, D.
2005-01-01
Polynomials, trigonometric polynomials, and rational functions are widely used for the discrete approximation of functions or simulation models.Often, it is known beforehand, that the underlying unknown function has certain properties, e.g. nonnegative or increasing on a certain region.However, the
ON THE FUNDAMENTAL GROUP OF OPEN MANIFOLDS WITH NONNEGATIVE RICCI CURVATURE
Institute of Scientific and Technical Information of China (English)
XU SENLIN; WANG ZUOQIN; YANG FANGYUN
2003-01-01
The authors establish some uniform estimates for the distance to halfway points of minimalgeodesics in terms of the distantce to end points on some types of Riemannian manifolds, andthen prove some theorems about the finite generation of fundamental group of Riemannianmanifold with nonnegative Ricci curvature, which support the famous Milnor conjecture.
Existence of non-negative solutions for nonlinear equations in the semi-positone case
Directory of Open Access Journals (Sweden)
Naji Yebari
2006-09-01
Full Text Available Using the fibring method we prove the existence of non-negative solution of the p-Laplacian boundary value problem $-Delta_pu=lambda f(u$, for any $lambda >0$ on any regular bounded domain of $mathbb{R}^N$, in the special case $f(t=t^q-1$.
On Nonnegative Solutions of Fractional q-Linear Time-Varying Dynamic Systems with Delayed Dynamics
Directory of Open Access Journals (Sweden)
M. De la Sen
2014-01-01
Full Text Available This paper is devoted to the investigation of nonnegative solutions and the stability and asymptotic properties of the solutions of fractional differential dynamic linear time-varying systems involving delayed dynamics with delays. The dynamic systems are described based on q-calculus and Caputo fractional derivatives on any order.
Linear Fractional Transformations of Nevanlinna Functions Associated with a Nonnegative Operator
Behrndt, Jussi; Hassi, Seppo; de Snoo, Henk; Wietsma, Rudi; Winkler, Henrik
2013-01-01
In the present paper a subclass of scalar Nevanlinna functions is studied, which coincides with the class of Weyl functions associated to a nonnegative symmetric operator of defect one in a Hilbert space. This class consists of all Nevanlinna functions that are holomorphic on (-a, 0) and all those N
Non-negatively curved 5-manifolds with almost maximal symmetry rank
Galaz-Garcia, Fernando
2011-01-01
We show that a closed, simply-connected, non-negatively curved 5-manifold admitting an effective, isometric $T^2$ action is diffeomorphic to one of $S^5$, $S^3\\times S^2$, $S^3\\tilde{\\times} S^2$ (the non-trivial $S^3$-bundle over $S^2$) or the Wu manifold $SU(3)/SO(3)$.
Reduction of Non-stationary Noise using a Non-negative Latent Variable Decomposition
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Larsen, Jan
2008-01-01
We present a method for suppression of non-stationary noise in single channel recordings of speech. The method is based on a non-negative latent variable decomposition model for the speech and noise signals, learned directly from a noisy mixture. In non-speech regions an over complete basis...
Steenge, Albert E.; Thissen, Mark J.P.M.
2005-01-01
Economic systems often are described in matrix form as x = Mx. We present a new theorem for systems of this type where M is square, nonnegative and indecomposable. The theorem discloses the existence of additional economic relations that have not been discussed in the literature up to now, and gives
DEFF Research Database (Denmark)
Volck, B; Price, P A; Johansen, J S;
1998-01-01
YKL-40, also called human cartilage glycoprotein-39 (HC gp-39), is a member of family 18 glycosyl hydrolases. YKL-40 is secreted by chondrocytes, synovial cells, and macrophages, and recently it has been reported that YKL-40 has a role as an autoantigen in rheumatoid arthritis (RA). The function ...
Matrix-exponential distributions in applied probability
Bladt, Mogens
2017-01-01
This book contains an in-depth treatment of matrix-exponential (ME) distributions and their sub-class of phase-type (PH) distributions. Loosely speaking, an ME distribution is obtained through replacing the intensity parameter in an exponential distribution by a matrix. The ME distributions can also be identified as the class of non-negative distributions with rational Laplace transforms. If the matrix has the structure of a sub-intensity matrix for a Markov jump process we obtain a PH distribution which allows for nice probabilistic interpretations facilitating the derivation of exact solutions and closed form formulas. The full potential of ME and PH unfolds in their use in stochastic modelling. Several chapters on generic applications, like renewal theory, random walks and regenerative processes, are included together with some specific examples from queueing theory and insurance risk. We emphasize our intention towards applications by including an extensive treatment on statistical methods for PH distribu...
Institute of Scientific and Technical Information of China (English)
夏春明; 郑建荣; J.Howell
2007-01-01
Constrained spectral non-negative matrix factorization (NMF) analysis of perturbed oscillatory process control loop variable data is performed for the isolation of multiple plant-wide oscillatory sources.The technique is described and demonstrated by analyzing data from both simulated and real plant data of a chemical process plant.Results show that the proposed approach can map multiple oscillatory sources onto the most appropriate control loops, and has superior performance in terms of reconstruction accuracy and intuitive understanding compared with spectral independent component analysis (ICA).
具有非负Ricci曲率的完备开流形%Complete Open Manifolds with Nonnegative Ricci Curvature
Institute of Scientific and Technical Information of China (English)
徐森林; 薛琼
2006-01-01
In this paper, we study complete open manifolds with nonnegative Ricci curvature and injectivity radius bounded from below. We find that this kind of manifolds are diffeomorphic to a Euclidean space when certain distance functions satisfy a reasonable condition.
A Conjugate Gradient Type Method for the Nonnegative Constraints Optimization Problems
Directory of Open Access Journals (Sweden)
Can Li
2013-01-01
Full Text Available We are concerned with the nonnegative constraints optimization problems. It is well known that the conjugate gradient methods are efficient methods for solving large-scale unconstrained optimization problems due to their simplicity and low storage. Combining the modified Polak-Ribière-Polyak method proposed by Zhang, Zhou, and Li with the Zoutendijk feasible direction method, we proposed a conjugate gradient type method for solving the nonnegative constraints optimization problems. If the current iteration is a feasible point, the direction generated by the proposed method is always a feasible descent direction at the current iteration. Under appropriate conditions, we show that the proposed method is globally convergent. We also present some numerical results to show the efficiency of the proposed method.
Technique for computing the PDFs and CDFs of non-negative infinitely divisible random variables
Veillette, Mark S
2010-01-01
We present a method for computing the PDF and CDF of a non-negative infinitely divisible random variable $X$. Our method uses the L\\'{e}vy-Khintchine representation of the Laplace transform $\\mathbb{E} e^{-\\lambda X} = e^{-\\phi(\\lambda)}$, where $\\phi$ is the Laplace exponent. We apply the Post-Widder method for Laplace transform inversion combined with a sequence convergence accelerator to obtain accurate results. We demonstrate this technique on several examples including the stable distribution, mixtures thereof, and integrals with respect to non-negative L\\'{e}vy processes. Software to implement this method is available from the authors and we illustrate its use at the end of the paper.
Instability of elliptic equations on compact Riemannian manifolds with non-negative Ricci curvature
Directory of Open Access Journals (Sweden)
Arnaldo S. Nascimento
2010-05-01
Full Text Available We prove the nonexistence of nonconstant local minimizers for a class of functionals, which typically appear in scalar two-phase field models, over smooth N-dimensional Riemannian manifolds without boundary and non-negative Ricci curvature. Conversely, for a class of surfaces possessing a simple closed geodesic along which the Gauss curvature is negative, we prove the existence of nonconstant local minimizers for the same class of functionals.
Expanding solitons with non-negative curvature operator coming out of cones
Schulze, Felix
2010-01-01
We show that a Ricci flow of any complete Riemannian manifold without boundary with bounded non-negative curvature operator and non-zero asymptotic volume ratio exists for all time and has constant asymptotic volume ratio. We show that there is a limit solution, obtained by scaling down this solution at a fixed point in space, which is an expanding soliton coming out of the asymptotic cone at infinity.
Non-negative submodular stochastic probing via stochastic contention resolution schemes
Adamczyk, Marek
2015-01-01
The abstract model of stochastic probing was presented by Gupta and Nagarajan (IPCO'13), and provides a unified view of a number of problems. Adamczyk, Sviridenko, Ward (STACS'14) gave better approximation for matroid environments and linear objectives. At the same time this method was easily extendable to settings, where the objective function was monotone submodular. However, the case of non-negative submodular function could not be handled by previous techniques. In this paper we address t...
(3, 1)*-Choosability of graphs of nonnegative characteristic without intersecting short cycles
Indian Academy of Sciences (India)
Haihui Zhang
2016-05-01
A graph is called (, )*-choosable if for every list assignment satisfying $|L(v)|\\geq k$ for all $v \\in V (G)$, there is an -coloring of such that each vertex of has at most neighbors colored with the same color as itself. In this paper, it is proved that every graph of nonnegative characteristic without intersecting -cycles for all = 3, 4, 5 is (3, 1)*-choosable.
An elementary proof of the Harnack inequality for non-negative infinity-superharmonic functions
Directory of Open Access Journals (Sweden)
Tilak Bhattacharya
2001-06-01
Full Text Available We present an elementary proof of the Harnack inequality for non-negative viscosity supersolutions of $Delta_{infty}u=0$. This was originally proven by Lindqvist and Manfredi using sequences of solutions of the $p$-Laplacian. We work directly with the $Delta_{infty}$ operator using the distance function as a test function. We also provide simple proofs of the Liouville property, Hopf boundary point lemma and Lipschitz continuity.
Robust ear recognition via nonnegative sparse representation of Gabor orientation information.
Zhang, Baoqing; Mu, Zhichun; Zeng, Hui; Luo, Shuang
2014-01-01
Orientation information is critical to the accuracy of ear recognition systems. In this paper, a new feature extraction approach is investigated for ear recognition by using orientation information of Gabor wavelets. The proposed Gabor orientation feature can not only avoid too much redundancy in conventional Gabor feature but also tend to extract more precise orientation information of the ear shape contours. Then, Gabor orientation feature based nonnegative sparse representation classification (Gabor orientation + NSRC) is proposed for ear recognition. Compared with SRC in which the sparse coding coefficients can be negative, the nonnegativity of NSRC conforms to the intuitive notion of combining parts to form a whole and therefore is more consistent with the biological modeling of visual data. Additionally, the use of Gabor orientation features increases the discriminative power of NSRC. Extensive experimental results show that the proposed Gabor orientation feature based nonnegative sparse representation classification paradigm achieves much better recognition performance and is found to be more robust to challenging problems such as pose changes, illumination variations, and ear partial occlusion in real-world applications.
On the continuity of the map square root of nonnegative isomorphisms in Hilbert spaces
Directory of Open Access Journals (Sweden)
Jeovanny de Jesus Muentes Acevedo
2015-06-01
Full Text Available Let H be a real (or complex Hilbert space. Every nonnegative operator L ∈ L(H admits a unique nonnegative square root R ∈ L(H, i.e., a nonnegative operator R ∈ L(H such that R2 = L. Let GL+ S (H be the set of nonnegative isomorphisms in L(H. First we will show that GL+ S (H is a convex (real Banach manifold. Denoting by L1/2 the nonnegative square root of L. In [3], Richard Bouldin proves that L1/2 depends continuously on L (this proof is non-trivial. This result has several applications. For example, it is used to find the polar decomposition of a bounded operator. This polar decomposition allows us to determine the positive and negative spectral subespaces of any self-adjoint operator, and moreover, allows us to define the Maslov index. The autor of the paper under review provides an alternative proof (and a little more simplified that L1/2 depends continuously on L, and moreover, he shows that the map is a homeomorphism. Resumen. Sea H un espacio de Hilbert real (o complejo. Todo operador no negativo L ∈ L(H admite una única raíz cuadrada no negativa R ∈ L(H, esto es, un operador no negativo R ∈ L(H tal que R2 = L. Sea GL+ S (H el conjunto de los isomorfismos no negativos en L(H. Primero probaremos que GL+ S (H es una variedad de Banach (real. Denotando como L1/2 la raíz cuadrada no negativa de L, en [3] Richard Bouldin prueba que L1/2 depende continuamente de L (esta prueba es no trivial. Este resultado tiene varias aplicaciones. Por ejemplo, es usado para encontrar la descomposición polar de un operador limitado. Esta descomposición polar nos lleva a determinar los subespacios espectrales positivos y negativos de cualquier operador autoadjunto, y además, lleva a definir el índice de Máslov. El autor de este artículo da una prueba alternativa (y un poco más simplificada de que L1/2 depende continuamente de L, y además, prueba que la aplicación es un homeomorfismo
Barbier, Mathieu; Gross, Marie-Sylvie; Aubart, Mélodie; Hanna, Nadine; Kessler, Ketty; Guo, Dong-Chuan; Tosolini, Laurent; Ho-Tin-Noe, Benoit; Regalado, Ellen; Varret, Mathilde; Abifadel, Marianne; Milleron, Olivier; Odent, Sylvie; Dupuis-Girod, Sophie; Faivre, Laurence; Edouard, Thomas; Dulac, Yves; Busa, Tiffany; Gouya, Laurent; Milewicz, Dianna M; Jondeau, Guillaume; Boileau, Catherine
2014-12-04
Thoracic aortic aneurysm and dissection (TAAD) is an autosomal-dominant disorder with major life-threatening complications. The disease displays great genetic heterogeneity with some forms allelic to Marfan and Loeys-Dietz syndrome, and an important number of cases still remain unexplained at the molecular level. Through whole-exome sequencing of affected members in a large TAAD-affected family, we identified the c.472C>T (p.Arg158(∗)) nonsense mutation in MFAP5 encoding the extracellular matrix component MAGP-2. This protein interacts with elastin fibers and the microfibrillar network. Mutation screening of 403 additional probands identified an additional missense mutation of MFAP5 (c.62G>T [p.Trp21Leu]) segregating with the disease in a second family. Functional analyses performed on both affected individual's cells and in vitro models showed that these two mutations caused pure or partial haploinsufficiency. Thus, alteration of MAGP-2, a component of microfibrils and elastic fibers, appears as an initiating mechanism of inherited TAAD.
Petrou, Petros; Makrygiannis, Apostolos K; Chalepakis, Georges
2008-01-01
Fras1 and the structurally related proteins Frem1, Frem2, and Frem3, comprise a novel family of extracellular matrix proteins, which localize in a similar fashion underneath the lamina densa of epithelial basement membranes. They are involved in the structural adhesion of the skin epithelium to its underlying mesenchyme. Deficiency in the individual murine Fras1/Frem genes gives rise to the bleb phenotype, which is equivalent to the human hereditary disorder Fraser syndrome, characterized by cryptophthalmos (hidden eyes), embryonic skin blistering, renal agenesis, and syndactyly. Recent studies revealed a functional cooperation between the Fras1/Frem gene products, in which Fras1, Frem1 and Frem2 are simultaneously stabilized at the lowermost region of the basement membrane by forming a macromolecular ternary complex. Loss of any of these proteins results in the collapse of the protein assembly, thus providing a molecular explanation for the highly similar phenotypic defects displayed by the respective mutant mice. Here, we summarize the current knowledge regarding the structure, function, and interplay between the proteins of the Fras1/Frem family and further propose a possible scenario for the evolution of the corresponding genes.
Directory of Open Access Journals (Sweden)
I. Cardoso
2008-01-01
Full Text Available Familial Amyloidotic Polyneuropathy (FAP is a disorder characterized by the extracellular deposition of fibrillar Transthyretin (TTR amyloid, with a special involvement of the peripheral nerve. Several extracellular matrix proteins have been found elevated in tissues from FAP patients, namely metalloproteinase-9 (MMP-9, neutrophil gelatinase associated lipocalin (NGAL and biglycan. In this work we assessed the levels of MMP-9, tissue inhibitor of metalloproteinase 1 (TIMP-1, NGAL, biglycan and chondroitin sulphate (CSPG in an FAP V30M TTR-related transgenic mouse model at different stages of TTR deposition and after two different treatment approaches to remove fibrillar deposits. Immunohistochemistry or RT-PCR analysis showed that biglycan was already increased in animals presenting TTR deposited in a non-fibrillar state, whereas MMP-9, TIMP-1, NGAL and CSPG were elevated only in mice with TTR amyloid deposits. Mice treated with doxycycline, a TTR fibril disrupter, presented lower levels of MMP-9, TIMP-1 and NGAL, suggestive of matrix recovery. Mice immunized with TTR Y78F to remove TTR deposition showed significantly lower levels of all the five tested markers, suggesting removal of fibrillar and non-fibrillar deposits. Cellular studies using oligomeric TTR showed induction of MMP-9 when compared to soluble TTR, large aggregates or fibrils. Furthermore, this induction was neutralized by an anti-receptor for advanced glycation end products (RAGE antibody, indicating RAGE engagement in this process. Further studies in a larger number of tissue samples will indicate the application of these ECM markers in parallel with Congo Red staining in tissue characterization of pre-clinical and clinical stages in FAP and other amyloidoses.
Ascent sequences and upper triangular matrices containing non-negative integers
Dukes, Mark
2009-01-01
This paper presents a bijection between ascent sequences and upper triangular matrices whose non-negative entries are such that all rows and columns contain at least one non-zero entry. We show the equivalence of several natural statistics on these structures under this bijection and prove that some of these statistics are equidistributed. Several special classes of matrices are shown to have simple formulations in terms of ascent sequences. Binary matrices are shown to correspond to ascent sequences with no two adjacent entries the same. Bidiagonal matrices are shown to be related to order-consecutive set partitions and a simple condition on the ascent sequences generate this class.
Non-negative Ricci curvature on closed manifolds under Ricci flow
Maximo, Davi
2009-01-01
In this short note we show that non-negative Ricci curvature is not preserved under Ricci flow for closed manifolds of dimensions four and above, strengthening a previous result of Knopf in \\cite{K} for complete non-compact manifolds of bounded curvature. This brings down to four dimensions a similar result B\\"ohm and Wilking have for dimensions twelve and above, \\cite{BW}. Moreover, the manifolds constructed here are \\Kahler manifolds and relate to a question raised by Xiuxiong Chen in \\cite{XC}, \\cite{XCL}.
Online multi-modal robust non-negative dictionary learning for visual tracking.
Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang
2015-01-01
Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.
Online multi-modal robust non-negative dictionary learning for visual tracking.
Directory of Open Access Journals (Sweden)
Xiang Zhang
Full Text Available Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.
Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation.
Dong, Weisheng; Fu, Fazuo; Shi, Guangming; Cao, Xun; Wu, Jinjian; Li, Guangyu; Li, Guangyu
2016-05-01
Hyperspectral imaging has many applications from agriculture and astronomy to surveillance and mineralogy. However, it is often challenging to obtain high-resolution (HR) hyperspectral images using existing hyperspectral imaging techniques due to various hardware limitations. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene. The estimation of the HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the prior knowledge of the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary representing prototype reflectance spectra vectors of the scene is first learned from the input LR image. Specifically, an efficient non-negative dictionary learning algorithm using the block-coordinate descent optimization technique is proposed. Then, the sparse codes of the desired HR hyperspectral image with respect to learned hyperspectral basis are estimated from the pair of LR and HR reference images. To improve the accuracy of non-negative sparse coding, a clustering-based structured sparse coding method is proposed to exploit the spatial correlation among the learned sparse codes. The experimental results on both public datasets and real LR hypspectral images suggest that the proposed method substantially outperforms several existing HR hyperspectral image recovery techniques in the literature in terms of both objective quality metrics and computational efficiency.
Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking
Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang
2015-01-01
Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality. PMID:25961715
Pavement crack detection combining non-negative feature with fast LoG in complex scene
Wang, Wanli; Zhang, Xiuhua; Hong, Hanyu
2015-12-01
Pavement crack detection is affected by much interference in the realistic situation, such as the shadow, road sign, oil stain, salt and pepper noise etc. Due to these unfavorable factors, the exist crack detection methods are difficult to distinguish the crack from background correctly. How to extract crack information effectively is the key problem to the road crack detection system. To solve this problem, a novel method for pavement crack detection based on combining non-negative feature with fast LoG is proposed. The two key novelties and benefits of this new approach are that 1) using image pixel gray value compensation to acquisit uniform image, and 2) combining non-negative feature with fast LoG to extract crack information. The image preprocessing results demonstrate that the method is indeed able to homogenize the crack image with more accurately compared to existing methods. A large number of experimental results demonstrate the proposed approach can detect the crack regions more correctly compared with traditional methods.
Directory of Open Access Journals (Sweden)
Chen Yidong
2011-10-01
Full Text Available Abstract Background Transcriptional regulation by transcription factor (TF controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task. Results We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method. Conclusions The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data.
Sharp maximal inequalities for the moments of martingales and non-negative submartingales
Osȩkowski, Adam
2012-01-01
In the paper we study sharp maximal inequalities for martingales and non-negative submartingales: if $f$, $g$ are martingales satisfying \\[|\\mathrm{d}g_n|\\leq|\\mathrm{d}f_n|,\\qquad n=0,1,2,...,\\] almost surely, then \\[\\Bigl\\|\\sup_{n\\geq0}|g_n|\\Bigr\\|_p\\leq p\\|f\\|_p,\\qquad p\\geq2,\\] and the inequality is sharp. Furthermore, if $\\alpha\\in[0,1]$, $f$ is a non-negative submartingale and $g$ satisfies \\[|\\mathrm{d}g_n|\\leq|\\mathrm{d}f_n|\\quad and\\quad |\\mathbb{E}(\\mathrm{d}g_{n+1}|\\mathcal {F}_n)|\\leq\\alpha\\mathbb{E}(\\mathrm{d}f_{n+1}|\\mathcal{F}_n),\\qquad n=0,1,2,...,\\] almost surely, then \\[\\Bigl\\|\\sup_{n\\geq0}|g_n|\\Bigr\\|_p\\leq(\\alpha+1)p\\|f\\|_p,\\qquad p\\geq2,\\] and the inequality is sharp. As an application, we establish related estimates for stochastic integrals and It\\^{o} processes. The inequalities strengthen the earlier classical results of Burkholder and Choi.
Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification
Directory of Open Access Journals (Sweden)
Xinzheng Zhang
2016-09-01
Full Text Available Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with ℓ 1 -regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.
Elwyn, Glyn; Bekkers, Marie-Jet; Tapp, Laura; Edwards, Adrian; Newcombe, Robert; Eriksson, Tina; Braspenning, Jozé; Kuch, Christine; Adzic, Zlata Ozvacic; Ayankogbe, Olayinka; Cvetko, Tatjana; In 't Veld, Kees; Karotsis, Antonis; Kersnik, Janko; Lefebvre, Luc; Mecini, Ilir; Petricek, Goranka; Pisco, Luis; Thesen, Janecke; Turón, José María; van Rossen, Edward; Grol, Richard
2010-12-01
Well-organised practices deliver higher-quality care. Yet there has been very little effort so far to help primary care organisations achieve higher levels of team performance and to help them identify and prioritise areas where quality improvement efforts should be concentrated. No attempt at all has been made to achieve a method which would be capable of providing comparisons--and the stimulus for further improvement--at an international level. The development of the International Family Practice Maturity Matrix took place in three phases: (1) selection and refinement of organisational dimensions; (2) development of incremental scales based on a recognised theoretical framework; and (3) testing the feasibility of the approach on an international basis, including generation of an automated web-based benchmarking system. This work has demonstrated the feasibility of developing an organisational assessment tool for primary care organisations that is sufficiently generic to cross international borders and is applicable across a diverse range of health settings, from state-organised systems to insurer-based health economies. It proved possible to introduce this assessment method in 11 countries in Europe and one in Africa, and to generate comparison benchmarks based on the data collected. The evaluation of the assessment process was uniformly positive with the view that the approach efficiently enables the identification of priorities for organisational development and quality improvement at the same time as motivating change by virtue of the group dynamics. We are not aware of any other organisational assessment method for primary care which has been 'born international,' and that has involved attention to theory, dimension selection and item refinement. The principal aims were to achieve an organisational assessment which gains added value by using interaction, engagement comparative benchmarks: aims which have been achieved. The next step is to achieve wider
Cai, Jinquan; Sun, Ying; Wang, Guangzhi; Li, Yongli; Li, Ruiyan; Feng, Yan; Han, Bo; Li, Jianlong; Tian, Yu; Yi, Liye; Jiang, Chuanlu
2016-01-01
Background Glioblastoma multiform (GBM) is the most common malignant primary brain tumor in adults. Radiotherapy plus concomitant and adjuvant TMZ chemotherapy is the current standard of care for patients with GBM. Matrix metalloproteinases (MMPs), a family of zinc-dependent endopeptidases, are key modulators of tumor invasion and metastasis due to their ECM degradation capacity. The aim of the present study was to identify the most informative MMP member in terms of prognostic and predictive ability for patients with primary GBM. Method The mRNA expression profiles of all MMP genes were obtained from the Chinese Glioma Genome Atlas (CGGA), the Repository for Molecular Brain Neoplasia Data (REMBRANDT) and the GSE16011 dataset. MGMT methylation status was also examined by pyrosequencing. The correlation of MMP9 expression with tumor progression was explored in glioma specimens of all grades. Kaplan–Meier analysis and Cox proportional hazards regression models were used to investigate the association of MMP9 expression with survival and response to temozolomide. Results MMP9 was the only significant prognostic factor in three datasets for primary glioblastoma patients. Our results indicated that MMP9 expression is correlated with glioma grade (p<0.0001). Additionally, low expression of MMP9 was correlated with better survival outcome (OS: p = 0.0012 and PFS: p = 0.0066), and MMP9 was an independent prognostic factor in primary GBM (OS: p = 0.027 and PFS: p = 0.032). Additionally, the GBM patients with low MMP9 expression benefited from temozolomide (TMZ) chemotherapy regardless of the MGMT methylation status. Conclusions Patients with primary GBMs with low MMP9 expression may have longer survival and may benefit from temozolomide chemotherapy. PMID:27022952
A hybrid-optimization method for large-scale non-negative full regualarization in image restoration
Guerrero, J.; Raydan, M.; Rojas, M.
2011-01-01
We describe a new hybrid-optimization method for solving the full-regularization problem of comput- ing both the regularization parameter and the corresponding regularized solution in 1-norm and 2-norm Tikhonov regularization with additional non-negativity constraints. The approach combines the simu
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This paper investigates the existence and multiplicity of nonnegative solutions to a singular nonlinear boundary value problem of second order differential equations with integral boundary conditions in a Banach space. The arguments are based on the construction of a nonempty bounded open convex set and fixed point index theory. Our nonlinearity possesses singularity and first derivative which makes it different with that in [10].
一个非负矩阵的不等式%An Inequality on Non-negative Matrix
Institute of Scientific and Technical Information of China (English)
张慧欣
2004-01-01
把Horst Alzer在Linear Algebra and its Application上发表的文章中得到关于指数为2的非负矩阵的不等式推广到指数为2k的一般情况.并给出了不等式中等号成立的充要条件.
Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding
Directory of Open Access Journals (Sweden)
Ying Chen
2014-05-01
Full Text Available Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE database. Compared with other widely used methods such as linear support vector machines (SVM, sparse representation-based classifier (SRC, nearest subspace classifier (NSC, K-nearest neighbor (KNN and radial basis function neural networks (RBFNN, the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.
Infinity Behavior of Bounded Subharmonic Functions on Ricci Non-negative Manifolds
Institute of Scientific and Technical Information of China (English)
Bao Qiang WU
2004-01-01
In this paper, we study the infinity behavior of the bounded subharmonic functions on a Ricci non-negative Riemannian manifold M. We first show that limr→∞r2/V(r) ∫B(r)△hdv = 0 if h is a bounded subharmonic function. If we further assume that the Laplacian decays pointwisely faster than quadratically we show that h approaches its supremun pointwisely at infinity, under certain auxiliary conditions on the volume growth of M. In particular, our result applies to the case when the Riemannian manifold has maximum volume growth. We also derive a representation formula in our paper, from which one can easily derive Yau's Liouville theorem on bounded harmonic functions.
Linear coloring of graphs embeddable in a surface of nonnegative characteristic
Institute of Scientific and Technical Information of China (English)
2009-01-01
A proper vertex coloring of a graph G is linear if the graph induced by the vertices of any two color classes is the union of vertex-disjoint paths. The linear chromatic number lc(G) of the graph G is the smallest number of colors in a linear coloring of G. In this paper, we prove that every graph G with girth g(G) and maximum degree Δ(G) that can be embedded in a surface of nonnegative characteristic has lc(G) = Δ(2G )+ 1 if there is a pair (Δ, g) ∈ {(13, 7), (9, 8), (7, 9), (5, 10), (3, 13)} such that G satisfies Δ(G) Δ and g(G) g.
Linear coloring of graphs embeddable in a surface of nonnegative characteristic
Institute of Scientific and Technical Information of China (English)
WANG WeiFan; LI Chao
2009-01-01
A proper vertex coloring of a graph G is linear if the graph induced by the vertices of any two color classes is the union of vertex-disjoint paths. The linear chromatic number lc(G) of the graph G is the smallest number of colors in a linear coloring of G. In this paper, we prove that every graph G with girth g(G) and maximum degree △(G) that can be embedded in a surface of nonnegative characteristic has lc(G) = 「△(G)/2」+ 1 if there is a pair (△,g) ∈ {(13, 7), (9, 8), (7, 9), (5, 10), (3, 13)} such that G satisfies △(G) ≥ △ and g(G) ≥ g.
Institute of Scientific and Technical Information of China (English)
Xiuxiong CHEN; Haozhao LI
2008-01-01
The authors show that the 2-non-negative traceless bisectional curvature is preserved along the K(a)hler-Ricci flow.The positivity of Ricci curvature is also preserved along the K(a)hler-Ricci flow with 2-non-negative traceless bisectional curvature.As a corollary,the K(a)hler-Ricci flow with 2-non-negative traceless bisectional curvature will converge to a K(a)hler-Ricci soliton in the sense of Cheeger-Gromov-Hausdorff topology if complex dimension n≥3.
Franklin, Joel N
2003-01-01
Mathematically rigorous introduction covers vector and matrix norms, the condition-number of a matrix, positive and irreducible matrices, much more. Only elementary algebra and calculus required. Includes problem-solving exercises. 1968 edition.
DEFF Research Database (Denmark)
Nielsen, Søren Føns Vind; Mørup, Morten
2014-01-01
of the component matrices. We examine three gene expression prediction scenarios based on data missing at random, whole genes missing and whole areas missing within a subject. We find that the column-wise updating approach also known as HALS performs the most efficient when fitting the model. We further observe...... that the non-negativity constrained CP model is able to predict gene expressions better than predicting by the subject average when data is missing at random. When whole genes and whole areas are missing it is in general better to predict by subject averages. However, we find that when whole genes are missing...... missing in our problem. Our analysis is based on the non-negativity constrained Canonical Polyadic (CP) decomposition where we handle the missing data using marginalization considering three prominent alternating least squares procedures; multiplicative updates, column-wise, and row-wise updating...
DEFF Research Database (Denmark)
Stattin, Eva-Lena; Wiklund, Fredrik; Lindblom, Karin;
2010-01-01
Osteochondritis dissecans is a disorder in which fragments of articular cartilage and subchondral bone dislodge from the joint surface. We analyzed a five-generation family in which affected members had autosomal-dominant familial osteochondritis dissecans. A genome-wide linkage analysis identifi...
Fast Nonnegative Deconvolution for Spike Train Inference From Population Calcium Imaging
Packer, Adam M.; Machado, Timothy A.; Sippy, Tanya; Babadi, Baktash; Yuste, Rafael; Paninski, Liam
2010-01-01
Fluorescent calcium indicators are becoming increasingly popular as a means for observing the spiking activity of large neuronal populations. Unfortunately, extracting the spike train of each neuron from a raw fluorescence movie is a nontrivial problem. This work presents a fast nonnegative deconvolution filter to infer the approximately most likely spike train of each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm runs in linear time, and is fast enough that even when simultaneously imaging >100 neurons, inference can be performed on the set of all observed traces faster than real time. Performing optimal spatial filtering on the images further refines the inferred spike train estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments. PMID:20554834
EEG source imaging with spatio-temporal tomographic nonnegative independent component analysis.
Valdés-Sosa, Pedro A; Vega-Hernández, Mayrim; Sánchez-Bornot, José Miguel; Martínez-Montes, Eduardo; Bobes, María Antonieta
2009-06-01
This article describes a spatio-temporal EEG/MEG source imaging (ESI) that extracts a parsimonious set of "atoms" or components, each the outer product of both a spatial and a temporal signature. The sources estimated are localized as smooth, minimally overlapping patches of cortical activation that are obtained by constraining spatial signatures to be nonnegative (NN), orthogonal, sparse, and smooth-in effect integrating ESI with NN-ICA. This constitutes a generalization of work by this group on the use of multiple penalties for ESI. A multiplicative update algorithm is derived being stable, fast and converging within seconds near the optimal solution. This procedure, spatio-temporal tomographic NN ICA (STTONNICA), is equally able to recover superficial or deep sources without additional weighting constraints as tested with simulations. STTONNICA analysis of ERPs to familiar and unfamiliar faces yields an occipital-fusiform atom activated by all faces and a more frontal atom that only is active with familiar faces. The temporal signatures are at present unconstrained but can be required to be smooth, complex, or following a multivariate autoregressive model.
Assessing instantaneous energy in the EEG: a non-negative, frequency-weighted energy operator.
O'Toole, John M; Temko, Andriy; Stevenson, Nathan
2014-01-01
Signal processing measures of instantaneous energy typically include only amplitude information. But measures that include both amplitude and frequency do better at assessing the energy required by the system to generate the signal, making them more sensitive measures to include in electroencephalogram (EEG) analysis. The Teager-Kaiser operator is a frequency-weighted measure that is frequently used in EEG analysis, although the operator is poorly defined in terms of common signal processing concepts. We propose an alternative frequency-weighted energy measure that uses the envelope of the derivative of the signal. This simple envelope- derivative operator has the advantage of being nonnegative, which when applied to a detection application in newborn EEG improves performance over the Teager-Kaiser operator: without post-processing filters, area-under the receiver-operating characteristic curve (AUC) is 0.57 for the Teager-Kaiser operator and 0.80 for the envelope-derivative operator. The envelope-derivative operator also satisfies important properties, similar to the Teager-Kaiser operator, such as tracking instantaneous amplitude and frequency.
Xi, Jianing; Li, Ao
2016-01-01
Recurrent copy number aberrations (RCNAs) in multiple cancer samples are strongly associated with tumorigenesis, and RCNA discovery is helpful to cancer research and treatment. Despite the emergence of numerous RCNA discovering methods, most of them are unable to detect RCNAs in complex patterns that are influenced by complicating factors including aberration in partial samples, co-existing of gains and losses and normal-like tumor samples. Here, we propose a novel computational method, called non-negative sparse singular value decomposition (NN-SSVD), to address the RCNA discovering problem in complex patterns. In NN-SSVD, the measurement of RCNA is based on the aberration frequency in a part of samples rather than all samples, which can circumvent the complexity of different RCNA patterns. We evaluate NN-SSVD on synthetic dataset by comparison on detection scores and Receiver Operating Characteristics curves, and the results show that NN-SSVD outperforms existing methods in RCNA discovery and demonstrate more robustness to RCNA complicating factors. Applying our approach on a breast cancer dataset, we successfully identify a number of genomic regions that are strongly correlated with previous studies, which harbor a bunch of known breast cancer associated genes.
Dynamical simulations of classical stochastic systems using matrix product states.
Johnson, T H; Clark, S R; Jaksch, D
2010-09-01
We adapt the time-evolving block decimation (TEBD) algorithm, originally devised to simulate the dynamics of one-dimensional quantum systems, to simulate the time evolution of nonequilibrium stochastic systems. We describe this method in detail; a system's probability distribution is represented by a matrix product state (MPS) of finite dimension and then its time evolution is efficiently simulated by repeatedly updating and approximately refactorizing this representation. We examine the use of MPS as an approximation method, looking at parallels between the interpretations of applying it to quantum state vectors and probability distributions. In the context of stochastic systems we consider two types of factorization for use in the TEBD algorithm: non-negative matrix factorization (NMF), which ensures that the approximate probability distribution is manifestly non-negative, and the singular value decomposition (SVD). Comparing these factorizations, we find the accuracy of the SVD to be substantially greater than current NMF algorithms. We then apply TEBD to simulate the totally asymmetric simple exclusion process (TASEP) for systems of up to hundreds of lattice sites in size. Using exact analytic results for the TASEP steady state, we find that TEBD reproduces this state such that the error in calculating expectation values can be made negligible even when severely compressing the description of the system by restricting the dimension of the MPS to be very small. Out of the steady state we show for specific observables that expectation values converge as the dimension of the MPS is increased to a moderate size.
Directory of Open Access Journals (Sweden)
Omnia Gamal El-Dien
2016-03-01
Full Text Available The open-pollinated (OP family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates’ offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of “half-sibling” in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure.
Gamal El-Dien, Omnia; Ratcliffe, Blaise; Klápště, Jaroslav; Porth, Ilga; Chen, Charles; El-Kassaby, Yousry A
2016-01-22
The open-pollinated (OP) family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates' offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of "half-sibling" in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench) Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure.
Matrix Factorization and Matrix Concentration
Mackey, Lester
2012-01-01
Motivated by the constrained factorization problems of sparse principal components analysis (PCA) for gene expression modeling, low-rank matrix completion for recommender systems, and robust matrix factorization for video surveillance, this dissertation explores the modeling, methodology, and theory of matrix factorization.We begin by exposing the theoretical and empirical shortcomings of standard deflation techniques for sparse PCA and developing alternative methodology more suitable for def...
Bodewig, E
1959-01-01
Matrix Calculus, Second Revised and Enlarged Edition focuses on systematic calculation with the building blocks of a matrix and rows and columns, shunning the use of individual elements. The publication first offers information on vectors, matrices, further applications, measures of the magnitude of a matrix, and forms. The text then examines eigenvalues and exact solutions, including the characteristic equation, eigenrows, extremum properties of the eigenvalues, bounds for the eigenvalues, elementary divisors, and bounds for the determinant. The text ponders on approximate solutions, as well
Extracellular matrix structure.
Theocharis, Achilleas D; Skandalis, Spyros S; Gialeli, Chrysostomi; Karamanos, Nikos K
2016-02-01
Extracellular matrix (ECM) is a non-cellular three-dimensional macromolecular network composed of collagens, proteoglycans/glycosaminoglycans, elastin, fibronectin, laminins, and several other glycoproteins. Matrix components bind each other as well as cell adhesion receptors forming a complex network into which cells reside in all tissues and organs. Cell surface receptors transduce signals into cells from ECM, which regulate diverse cellular functions, such as survival, growth, migration, and differentiation, and are vital for maintaining normal homeostasis. ECM is a highly dynamic structural network that continuously undergoes remodeling mediated by several matrix-degrading enzymes during normal and pathological conditions. Deregulation of ECM composition and structure is associated with the development and progression of several pathologic conditions. This article emphasizes in the complex ECM structure as to provide a better understanding of its dynamic structural and functional multipotency. Where relevant, the implication of the various families of ECM macromolecules in health and disease is also presented.
Eckhard, Ulrich; Huesgen, Pitter F; Schilling, Oliver; Bellac, Caroline L; Butler, Georgina S; Cox, Jennifer H; Dufour, Antoine; Goebeler, Verena; Kappelhoff, Reinhild; Keller, Ulrich Auf dem; Klein, Theo; Lange, Philipp F; Marino, Giada; Morrison, Charlotte J; Prudova, Anna; Rodriguez, David; Starr, Amanda E; Wang, Yili; Overall, Christopher M
2016-01-01
Secreted and membrane tethered matrix metalloproteinases (MMPs) are key homeostatic proteases regulating the extracellular signaling and structural matrix environment of cells and tissues. For drug targeting of proteases, selectivity for individual molecules is highly desired and can be met by high yield active site specificity profiling. Using the high throughput Proteomic Identification of protease Cleavage Sites (PICS) method to simultaneously profile both the prime and non-prime sides of the cleavage sites of nine human MMPs, we identified more than 4300 cleavages from P6 to P6' in biologically diverse human peptide libraries. MMP specificity and kinetic efficiency were mainly guided by aliphatic and aromatic residues in P1' (with a ~32-93% preference for leucine depending on the MMP), and basic and small residues in P2' and P3', respectively. A wide differential preference for the hallmark P3 proline was found between MMPs ranging from 15 to 46%, yet when combined in the same peptide with the universally preferred P1' leucine, an unexpected negative cooperativity emerged. This was not observed in previous studies, probably due to the paucity of approaches that profile both the prime and non-prime sides together, and the masking of subsite cooperativity effects by global heat maps and iceLogos. These caveats make it critical to check for these biologically highly important effects by fixing all 20 amino acids one-by-one in the respective subsites and thorough assessing of the inferred specificity logo changes. Indeed an analysis of bona fide MEROPS physiological substrate cleavage data revealed that of the 37 natural substrates with either a P3-Pro or a P1'-Leu only 5 shared both features, confirming the PICS data. Upon probing with several new quenched-fluorescent peptides, rationally designed on our specificity data, the negative cooperativity was explained by reduced non-prime side flexibility constraining accommodation of the rigidifying P3 proline with
Dai, Yimian; Wu, Yiquan; Song, Yu; Guo, Jun
2017-03-01
To further enhance the small targets and suppress the heavy clutters simultaneously, a robust non-negative infrared patch-image model via partial sum minimization of singular values is proposed. First, the intrinsic reason behind the undesirable performance of the state-of-the-art infrared patch-image (IPI) model when facing extremely complex backgrounds is analyzed. We point out that it lies in the mismatching of IPI model's implicit assumption of a large number of observations with the reality of deficient observations of strong edges. To fix this problem, instead of the nuclear norm, we adopt the partial sum of singular values to constrain the low-rank background patch-image, which could provide a more accurate background estimation and almost eliminate all the salient residuals in the decomposed target image. In addition, considering the fact that the infrared small target is always brighter than its adjacent background, we propose an additional non-negative constraint to the sparse target patch-image, which could not only wipe off more undesirable components ulteriorly but also accelerate the convergence rate. Finally, an algorithm based on inexact augmented Lagrange multiplier method is developed to solve the proposed model. A large number of experiments are conducted demonstrating that the proposed model has a significant improvement over the other nine competitive methods in terms of both clutter suppressing performance and convergence rate.
Craps, Ben; Nguyen, Kévin
2016-01-01
Matrix quantum mechanics offers an attractive environment for discussing gravitational holography, in which both sides of the holographic duality are well-defined. Similarly to higher-dimensional implementations of holography, collapsing shell solutions in the gravitational bulk correspond in this setting to thermalization processes in the dual quantum mechanical theory. We construct an explicit, fully nonlinear supergravity solution describing a generic collapsing dilaton shell, specify the holographic renormalization prescriptions necessary for computing the relevant boundary observables, and apply them to evaluating thermalizing two-point correlation functions in the dual matrix theory.
基于距离像时频非负稀疏编码的SAR目标识别%SAR ATR based on HRRP time-frequency non-negative sparse coding
Institute of Scientific and Technical Information of China (English)
张新征; 刘书君; 秦建红; 黄培康
2014-01-01
A new approach to classify synthetic aperture radar (SAR)targets is presented based on high range resolution profile (HRRP)time-frequency non-negative sparse coding (NNSC).Firstly,complex SAR target images are converted into HRRPs.And the non-negative time-frequency matrix for each profile is ob-tained by using adaptive Gaussian representation (AGR).Secondly,NNSC is applied to learn target time-fre-quency dictionary.Feature vectors are constructed by proj ecting each HRR profile time-frequency matrix to the time-frequency dictionary.Finally,the target classification decision is found with the support vector machine. To demonstrate the performance of the proposed approach,experiments are performed with SAR database re-leased publicly by moving and stationary target acquisition and recognition (MSTAR).The experiment results support the effectiveness of the proposed technique for SAR target classification.%提出了一种基于目标高分辨距离像时频域非负稀疏编码的合成孔径雷达（synthetic aperture radar， SAR）目标识别方法。首先，将目标的SAR复图像转换为高分辨距离像。然后，采用自适应高斯基表示方法计算每个距离像的非负时频矩阵。其次，对训练目标所有距离像的时频矩阵采用非负稀疏编码方法学习时频字典。在目标识别中，通过将每个距离像的时频矩阵投影到低维的时频字典上来提取特征矢量。最后，在提取特征矢量的基础上，通过支撑向量机目标识别决策实现目标识别。采用美国“运动和静止目标获取与识别计划”公开发布的SAR图像数据库进行算法验证实验。实验结果说明了提出方法的有效性。
1980-09-29
FOUNDATIONS OF EIGENVALUE DISTRIBUTION THEORY FOR GENERAL A NON--ETC(U) SEP 80 M MARCUS, M GOLDBERG, M NEWMAN AFOSR-79-0127 UNCLASSIFIED AFOSR-TR-80...September 1980 Title of Research: Foundations of Eigenvalue Distribution Theory for General & Nonnegative Matrices, Stability Criteria for Hyperbolic
Zhan, Xingzhi
2002-01-01
The main purpose of this monograph is to report on recent developments in the field of matrix inequalities, with emphasis on useful techniques and ingenious ideas. Among other results this book contains the affirmative solutions of eight conjectures. Many theorems unify or sharpen previous inequalities. The author's aim is to streamline the ideas in the literature. The book can be read by research workers, graduate students and advanced undergraduates.
Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun
2017-08-21
Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF (3) here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.
Two pitfalls of BOLD fMRI magnitude-based neuroimage analysis: non-negativity and edge effect.
Chen, Zikuan; Calhoun, Vince D
2011-08-15
BOLD fMRI is accepted as a noninvasive imaging modality for neuroimaging and brain mapping. A BOLD fMRI dataset consists of magnitude and phase components. Currently, only the magnitude is used for neuroimage analysis. In this paper, we show that the fMRI-magnitude-based neuroimage analysis may suffer two pitfalls: one is that the magnitude is non-negative and cannot differentiate positive from negative BOLD activity; the other is an edge effect that may manifest as an edge enhancement or a spatial interior dip artifact at a local uniform BOLD region. We demonstrate these pitfalls via numeric simulations using a BOLD fMRI model and also via a phantom experiment. We also propose a solution by making use of the fMRI phase image, the counterpart of the fMRI magnitude.
Directory of Open Access Journals (Sweden)
Sujoy Roy
2017-08-01
Full Text Available In this study, we developed and evaluated a novel text-mining approach, using non-negative tensor factorization (NTF, to simultaneously extract and functionally annotate transcriptional modules consisting of sets of genes, transcription factors (TFs, and terms from MEDLINE abstracts. A sparse 3-mode term × gene × TF tensor was constructed that contained weighted frequencies of 106,895 terms in 26,781 abstracts shared among 7,695 genes and 994 TFs. The tensor was decomposed into sub-tensors using non-negative tensor factorization (NTF across 16 different approximation ranks. Dominant entries of each of 2,861 sub-tensors were extracted to form term–gene–TF annotated transcriptional modules (ATMs. More than 94% of the ATMs were found to be enriched in at least one KEGG pathway or GO category, suggesting that the ATMs are functionally relevant. One advantage of this method is that it can discover potentially new gene–TF associations from the literature. Using a set of microarray and ChIP-Seq datasets as gold standard, we show that the precision of our method for predicting gene–TF associations is significantly higher than chance. In addition, we demonstrate that the terms in each ATM can be used to suggest new GO classifications to genes and TFs. Taken together, our results indicate that NTF is useful for simultaneous extraction and functional annotation of transcriptional regulatory networks from unstructured text, as well as for literature based discovery. A web tool called Transcriptional Regulatory Modules Extracted from Literature (TREMEL, available at http://binf1.memphis.edu/tremel, was built to enable browsing and searching of ATMs.
Bhatia, Rajendra
1997-01-01
A good part of matrix theory is functional analytic in spirit. This statement can be turned around. There are many problems in operator theory, where most of the complexities and subtleties are present in the finite-dimensional case. My purpose in writing this book is to present a systematic treatment of methods that are useful in the study of such problems. This book is intended for use as a text for upper division and gradu ate courses. Courses based on parts of the material have been given by me at the Indian Statistical Institute and at the University of Toronto (in collaboration with Chandler Davis). The book should also be useful as a reference for research workers in linear algebra, operator theory, mathe matical physics and numerical analysis. A possible subtitle of this book could be Matrix Inequalities. A reader who works through the book should expect to become proficient in the art of deriving such inequalities. Other authors have compared this art to that of cutting diamonds. One first has to...
From Totally Unimodular to Balanced O, +-1 Matrices: A Family of Integer Polytopes,
1992-07-07
and DMS-9000376. 1Dipartimento di Matematica Pura ed Applicata, Universiti di Padova, Via Belzoni 7, 35131 Padova, Italy. 2Carnegie Mellon University... vectors having components pi(A), ni(A) and ti(A) respectively. We write d to denote a vector all of whose compo- nents are equal to d. The matrix A is...statements are equivalent for a 0, ±1 matrix A and a nonnegative integral vector c. (i) A does not contain a submatrix A’ E 7-1 such that t(A’) < 2c
Belitsky, A V
2016-01-01
The Operator Product Expansion for null polygonal Wilson loop in planar maximally supersymmetric Yang-Mills theory runs systematically in terms of multiparticle pentagon transitions which encode the physics of excitations propagating on the color flux tube ending on the sides of the four-dimensional contour. Their dynamics was unravelled in the past several years and culminated in a complete description of pentagons as an exact function of the 't Hooft coupling. In this paper we provide a solution for the last building block in this program, the SU(4) matrix structure arising from internal symmetry indices of scalars and fermions. This is achieved by a recursive solution of the Mirror and Watson equations obeyed by the so-called singlet pentagons and fixing the form of the twisted component in their tensor decomposition. The non-singlet, or charged, pentagons are deduced from these by a limiting procedure.
Kardaras, Constantinos
2012-01-01
We introduce the concepts of max-closedness and outer support points of convex sets in the nonnegative orthant of the topological vector space of all random variables built over a probability space, equipped with a topology consistent with convergence of sequences in probability. Max-closedness asks that maximal elements of the closure of a set already lie on the set. We show that outer support points arise naturally as optimizers of concave monotone maximization problems. It is further shown that the set of outer support points of a convex, max-closed and bounded set of nonnegative random variables is dense in the set of its maximal elements, which can be regarded as a version of the celebrated Bishop-Phelps theorem in a space that even fails to be locally convex.
THE SOLVABILITY CONDITIONS FOR THE INVERSE PROBLEM OF BISYMMETRIC NONNEGATIVE DEFINITE MATRICES
Institute of Scientific and Technical Information of China (English)
Dong-xiu Xie; Lei Zhang; Xi-yan Hu
2000-01-01
A = (aij) ∈ Rn×n is termed bisymmetric matrix if aij = aji = an-j+1,n-i+1, i,j = 1,2...n. We denote the set of all n × n bisymmetric matrices by BSRn×n. This paper is mainly concerned with solving the following two problems: Problem I. Given X, B ∈ Rn×m, find A ∈ Pn such that AX = B, where Pn = {A ∈ BSRn×n] xTAx ＜ 0, Ax ∈ Rn}. Problem II. Given A* ∈ Rn×n, find A ∈ SE such that where ‖ ·‖F is Frobenius norm, and SE denotes the solution set of problem I. The necessary and sufficient conditions for the solvability of problem I have been studied. The general form of SE has been given. For problem II the expression of the solution has been provided.
Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model
Vazifedan, Turaj; Shitan, Mahendran
Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.
Kargın, Levent; Kurt, Veli
2015-01-01
In this study, obtaining the matrix analog of the Euler's reflection formula for the classical gamma function we expand the domain of the gamma matrix function and give a infinite product expansion of sinπxP. Furthermore we define Riemann zeta matrix function and evaluate some other matrix integrals. We prove a functional equation for Riemann zeta matrix function.
Recognizing Facial Expression with Non-negative Matrix Factorization%使用非负矩阵分解方法识别脸部表情
Institute of Scientific and Technical Information of China (English)
陈军宁; 王戈; 潘麒安
2009-01-01
在论文中两种图像识别的方法即非负矩阵分解方法(NMF)和主成分分析方法(PCA)被适用于认识三种基本脸部表情的数据库.三种表情是:高兴,惊恐和中性表达.采用NMF和PCA方法提取的脸部表情特征被分别独立地送到最大相关分类比较不同的结果.基于CMU脸数据库的实验方面证明,我们得到的结论是NMF有了比较好的表现.
Directory of Open Access Journals (Sweden)
Linda Christian Carrijo-Carvalho
2012-01-01
Full Text Available Lipocalin family members have been implicated in development, regeneration, and pathological processes, but their roles are unclear. Interestingly, these proteins are found abundant in the venom of the Lonomia obliqua caterpillar. Lipocalins are β-barrel proteins, which have three conserved motifs in their amino acid sequence. One of these motifs was shown to be a sequence signature involved in cell modulation. The aim of this study is to investigate the effects of a synthetic peptide comprising the lipocalin sequence motif in fibroblasts. This peptide suppressed caspase 3 activity and upregulated Bcl-2 and Ki-67, but did not interfere with GPCR calcium mobilization. Fibroblast responses also involved increased expression of proinflammatory mediators. Increase of extracellular matrix proteins, such as collagen, fibronectin, and tenascin, was observed. Increase in collagen content was also observed in vivo. Results indicate that modulation effects displayed by lipocalins through this sequence motif involve cell survival, extracellular matrix remodeling, and cytokine signaling. Such effects can be related to the lipocalin roles in disease, development, and tissue repair.
Directory of Open Access Journals (Sweden)
Magda Dimenstein
2009-03-01
Full Text Available A Reforma Psiquiátrica busca superar as intervenções tradicionalmente hospitalocêntricas e medicalizantes em relação à "loucura". Para isso, visa implantar estratégias de cuidado territoriais e integrais, ancorados em novos saberes e valores culturais. Nessa perspectiva, o Apoio Matricial surge como proposta para articular os cuidados em saúde mental à Atenção Básica. Este trabalho objetiva discutir a perspectiva de técnicos de Unidades de Saúde da Família (USF do município de Natal, RN, acerca dessa proposta. Foram realizadas entrevistas com oito técnicos da USF do Distrito Sanitário Leste da cidade. A partir dos resultados observamos que não há clareza acerca da proposta de Apoio Matricial (AM e há uma forte demanda cotidiana de saúde mental não acolhida, pois os entrevistados não se sentem capacitados para tal e indicam a necessidade de apoio e instrumentalização nesse campo. Além disso, as possibilidades de referenciamento são pequenas em função da precariedade da rede de serviços substitutivos e destes com a rede do Sistema Único de Saúde (SUS como um todo. O trabalho compartilhado com o Centro de Atenção Psicossocial (CAPS é ainda uma promessa.The Psychiatric Reform aims to overcome the traditional interventions concerning "madness", which are hospital-centered and medicine-based. To achieve this, its objective is to implement territorial and integral care strategies, supported by new knowledge and cultural values. In this perspective, the Matrix Support emerges as a proposal to articulate mental healthcare with Primary Care. This study aims to discuss the perspective of technicians from Unidades de Saúde da Família (USF - Family Health Units of the municipality of Natal, state of Rio Grande do Norte, concerning this proposal. Interviews with eight technicians of the USF of the East Sanitary District of the city were conducted. Based on the results, we observed that the proposal for Matrix Support is
DEFF Research Database (Denmark)
Rasmussen, Andreas H; Rasmussen, Hanne B; Silahtaroglu, Asli
2017-01-01
downstream signalling in the neuron. The postsynaptic density, a highly specialized matrix, which is attached to the postsynaptic membrane, controls this downstream signalling. The postsynaptic density also resets the synapse after each synaptic firing. It is composed of numerous proteins including a family...... in the postsynapse, the DLGAP family seems to play a vital role in synaptic scaling by regulating the turnover of both ionotropic and metabotropic glutamate receptors in response to synaptic activity. DLGAP family has been directly linked to a variety of psychological and neurological disorders. In this review we...... focus on the direct and indirect role of DLGAP family on schizophrenia as well as other brain diseases....
Jozwik, Kamila M; Kriegeskorte, Nikolaus; Mur, Marieke
2016-03-01
Object similarity, in brain representations and conscious perception, must reflect a combination of the visual appearance of the objects on the one hand and the categories the objects belong to on the other. Indeed, visual object features and category membership have each been shown to contribute to the object representation in human inferior temporal (IT) cortex, as well as to object-similarity judgments. However, the explanatory power of features and categories has not been directly compared. Here, we investigate whether the IT object representation and similarity judgments are best explained by a categorical or a feature-based model. We use rich models (>100 dimensions) generated by human observers for a set of 96 real-world object images. The categorical model consists of a hierarchically nested set of category labels (such as "human", "mammal", and "animal"). The feature-based model includes both object parts (such as "eye", "tail", and "handle") and other descriptive features (such as "circular", "green", and "stubbly"). We used non-negative least squares to fit the models to the brain representations (estimated from functional magnetic resonance imaging data) and to similarity judgments. Model performance was estimated on held-out images not used in fitting. Both models explained significant variance in IT and the amounts explained were not significantly different. The combined model did not explain significant additional IT variance, suggesting that it is the shared model variance (features correlated with categories, categories correlated with features) that best explains IT. The similarity judgments were almost fully explained by the categorical model, which explained significantly more variance than the feature-based model. The combined model did not explain significant additional variance in the similarity judgments. Our findings suggest that IT uses features that help to distinguish categories as stepping stones toward a semantic representation
Perturbing Misiurewicz Parameters in the Exponential Family
Dobbs, Neil
2015-04-01
In one-dimensional real and complex dynamics, a map whose post-singular (or post-critical) set is bounded and uniformly repelling is often called a Misiurewicz map. In results hitherto, perturbing a Misiurewicz map is likely to give a non-hyperbolic map, as per Jakobson's Theorem for unimodal interval maps. This is despite genericity of hyperbolic parameters (at least in the interval setting). We show the contrary holds in the complex exponential family Misiurewicz maps are Lebesgue density points for hyperbolic parameters. As a by-product, we also show that Lyapunov exponents almost never exist for exponential Misiurewicz maps. The lower Lyapunov exponent is -∞ almost everywhere. The upper Lyapunov exponent is non-negative and depends on the choice of metric.
CSR expansions of matrix powers in max algebra
Sergeev, Sergei
2009-01-01
We study the behavior of max-algebraic powers of a reducible nonnegative n by n matrix A. We show that for t>3n^2, the powers A^t can be expanded in max-algebraic powers of the form CS^tR, where C and R are extracted from columns and rows of certain Kleene stars and S is diadonally similar to a Boolean matrix. We study the properties of individual terms and show that all terms, for a given t>3n^2, can be found in O(n^4 log n) operations. We show that the powers have a well-defined ultimate behavior, where certain terms are totally or partially suppressed, thus leading to ultimate CS^tR terms and the corresponding ultimate expansion. We apply this expansion to the question whether {A^ty, t>0} is ultimately linear periodic for each starting vector y, showing that this question can be also answered in O(n^4 log n) time. We give examples illustrating our main results.
W准对称非负定矩阵反问题的解%Solutions of inverse problems for W-para-symmetric nonnegative definite matrices
Institute of Scientific and Technical Information of China (English)
唐耀平; 周立平
2015-01-01
研究了 W 准对称非负定矩阵反问题的解，得到了这一问题有解的充分必要条件，并在有解的情况下给出了解的一般表达式和算法例子。%The solutions of inverse problems for W-para-symmetric nonnegative definite matrices are studies, and the necessary and sufficient conditions for the solvability of this problem are obtained. The expression and the example of general solution about this problem are given under case of having solution.
Face Super-resolution With Non-negative Featrue Basis Constraint%非负特征基约束的人脸超分辨率
Institute of Scientific and Technical Information of China (English)
兰诚栋; 胡瑞敏; 韩镇; 卢涛
2011-01-01
Principal Component Analysis (PC A) is commonly used for human face images representation in face super-resolution. But the features extracted by PCA are holistic and difficult to have semantic interpretation. In order to synthesize a better super-resolution face image with the results of the face images representation, we propose face a super-resolution algorithm with non-negative featrue basis constraint The algorithm uses the NMF to obtain non-negative featrue basis of face sample images, and the target image is regularized by Markov random fields, with maximum a posteriori probability approach. Finally, the steepest descent method is used to optimize non-negative featrue basis coefficient of high-resolution image. Experimental results show that, in the subjective and objective quality, the face super-resolution algorithm with non-negative feature basis constrait performs better than PCA-based algorithms.%主成分分析(PCA)是人脸超分辨率申常用的人脸图像表达方法,但是PCA方法的特征是整体的且难以语义解释.为了使表达的结果更好地用于合成超分辨率人脸图像,提出一种非负特征基约束的人脸超分辨率算法.该算法利用非负矩阵分解(NMF)获取样本人脸图像的非负特征基,结合最大后验概率的方法,对目标图像进行马尔可夫随机场正则约束,最速下降法优化得到高分辨率人脸图像的非负特征基系数.实验结果表明,在主客观质量上,非负特征基约束的人脸超分辨率算法的性能胜过基于PCA的算法.
Institute of Scientific and Technical Information of China (English)
贾梅; 刘锡平
2008-01-01
The paper studies the existence of three nonnegative solutions to a type of threepoint boundary value problem for second-order impulsive differential equations, and obtains the sufficient conditions for existence of three nonnegative solutions by means of the Leggett-Williams's fixed point theorem.
DEFF Research Database (Denmark)
Petersen, Kaare Brandt; Pedersen, Michael Syskind
Matrix identities, relations and approximations. A desktop reference for quick overview of mathematics of matrices.......Matrix identities, relations and approximations. A desktop reference for quick overview of mathematics of matrices....
Matrix with Prescribed Eigenvectors
Ahmad, Faiz
2011-01-01
It is a routine matter for undergraduates to find eigenvalues and eigenvectors of a given matrix. But the converse problem of finding a matrix with prescribed eigenvalues and eigenvectors is rarely discussed in elementary texts on linear algebra. This problem is related to the "spectral" decomposition of a matrix and has important technical…
Meijer, Martin Jan-Willem
2009-01-01
Crohn’s disease (CD) is characterized by chronic, patchy, transmural inflammation of the entire gastrointestinal tract, while ulcerative colitis (UC) is manifested by chronic, continuous, superficial inflammation of the colon. Matrix metalloproteinases (MMPs) constitute a family of matrix degrading
The application of Positive Matrix Factorization (PMF) to eco-efficiency analysis.
Wu, Jiaying; Wu, Zhijun; Holländer, Robert
2012-05-15
A new method for weighting and aggregating eco-efficiency indicators is of the utmost importance, if researchers in the field are to provide simplified and physically meaningful information to policy makers. To date, there is still considerable debate over which weighting and aggregating methods to use in this context. We apply a new variant of factor analysis, Positive Matrix Factorization (PMF), to a simple eco-efficiency analysis case study. PMF constrains its solutions to be non-negative, providing two important advantages over traditional factor analysis (FA) or principal component analysis (PCA): the rotational ambiguity of the solution space is reduced, and all the results are guaranteed to be physically meaningful. We suggest that PMF is better choice than either FA or PCA for eco-efficiency indicators, especially when dealing with complex social-economic and environmental data.
Hypercontractivity in finite-dimensional matrix algebras
Energy Technology Data Exchange (ETDEWEB)
Junge, Marius, E-mail: junge@math.uiuc.edu [Department of Mathematics, University of Illinois at Urbana-Champaign, 1409 W. Green St., Urbana, Illinois 61891 (United States); Palazuelos, Carlos, E-mail: carlospalazuelos@ucm.es [Instituto de Ciencias Matemáticas, CSIC-UAM-UC3M-UCM, Universidad Complutense de Madrid, Facultad de Ciencias Matemáticas, Plaza de Ciencias s/n, 28040 Madrid (Spain); Parcet, Javier, E-mail: javier.parcet@icmat.es; Perrin, Mathilde, E-mail: mathilde.perrin@icmat.es [Instituto de Ciencias Matemáticas, CSIC-UAM-UC3M-UCM, Consejo Superior de Investigaciones Científicas, C/ Nicolás Cabrera 13-15, 28049 Madrid (Spain)
2015-02-15
We obtain hypercontractivity estimates for a large class of semigroups defined on finite-dimensional matrix algebras M{sub n}. These semigroups arise from Poisson-like length functions ψ on ℤ{sub n} × ℤ{sub n} and provide new hypercontractive families of quantum channels when ψ is conditionally negative. We also study the optimality of our estimates.
Physiology and pathophysiology of matrix metalloproteases
Klein, T.; Bischoff, R.
2011-01-01
Matrix metalloproteases (MMPs) comprise a family of enzymes that cleave protein substrates based on a conserved mechanism involving activation of an active site-bound water molecule by a Zn(2+) ion. Although the catalytic domain of MMPs is structurally highly similar, there are many differences with
Physiology and pathophysiology of matrix metalloproteases
Klein, T; Bischoff, Rainer
2010-01-01
Matrix metalloproteases (MMPs) comprise a family of enzymes that cleave protein substrates based on a conserved mechanism involving activation of an active site-bound water molecule by a Zn(2+) ion. Although the catalytic domain of MMPs is structurally highly similar, there are many differences with
DEFF Research Database (Denmark)
Christoffersen, Mogens
The situation in the family preceding a family separation is studied here, to identify risk factors for family dissolution. Information registers covering prospective statistics about health aspects, demographic variables, family violence, self-destructive behaviour, unemployment, and the spousal...
DEFF Research Database (Denmark)
Christoffersen, Mogens
The situation in the family preceding a family separation is studied here, to identify risk factors for family dissolution. Information registers covering prospective statistics about health aspects, demographic variables, family violence, self-destructive behaviour, unemployment, and the spousal...
... With Family and Friends > Family Life Request Permissions Family Life Approved by the Cancer.Net Editorial Board , ... your outlook on the future. Friends and adult family members The effects of cancer on your relationships ...
Bidifferential Calculus, Matrix SIT and Sine-Gordon Equations
Directory of Open Access Journals (Sweden)
A. Dimakis
2011-01-01
Full Text Available We express a matrix version of the self-induced transparency (SIT equations in the bidifferential calculus framework. An infinite family of exact solutions is then obtained by application of a general result that generates exact solutions from solutions of a linear system of arbitrary matrix size. A side result is a solution formula for the sine-Gordon equation.
Parallelism in matrix computations
Gallopoulos, Efstratios; Sameh, Ahmed H
2016-01-01
This book is primarily intended as a research monograph that could also be used in graduate courses for the design of parallel algorithms in matrix computations. It assumes general but not extensive knowledge of numerical linear algebra, parallel architectures, and parallel programming paradigms. The book consists of four parts: (I) Basics; (II) Dense and Special Matrix Computations; (III) Sparse Matrix Computations; and (IV) Matrix functions and characteristics. Part I deals with parallel programming paradigms and fundamental kernels, including reordering schemes for sparse matrices. Part II is devoted to dense matrix computations such as parallel algorithms for solving linear systems, linear least squares, the symmetric algebraic eigenvalue problem, and the singular-value decomposition. It also deals with the development of parallel algorithms for special linear systems such as banded ,Vandermonde ,Toeplitz ,and block Toeplitz systems. Part III addresses sparse matrix computations: (a) the development of pa...
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Matrix-bound phosphine was determined in the Jiaozhou Bay coastal sediment, in prawn-pond bottom soil, in the eutrophic lake Wulongtan, in the sewage sludge and in paddy soil as well. Results showed that matrix-bound phosphine levels in freshwater and coastal sediment, as well as in sewage sludge, are significantly higher than that in paddy soil. The correlation between matrix bound phosphine concentrations and organic phosphorus contents in sediment samples is discussed.
Theys, Céline; Dobigeon, Nicolas; Richard, Cédric; Tourneret, Jean-Yves; Ferrari, André
2013-01-01
This paper addresses the problem of minimizing a convex cost function under non-negativity and equality constraints, with the aim of solving the linear unmixing problem encountered in hyperspectral imagery. This problem can be formulated as a linear regression problem whose regression coefficients (abundances) satisfy sum-to-one and positivity constraints. A normalized scaled gradient iterative method (NSGM) is proposed for estimating the abundances of the linear mixing model. The positivity constraint is ensured by the Karush Kuhn Tucker conditions whereas the sum-to-one constraint is fulfilled by introducing normalized variables in the algorithm. The convergence is ensured by a one-dimensional search of the step size. Note that NSGM can be applied to any convex cost function with non negativity and flux constraints. In order to compare the NSGM with the well-known fully constraint least squares (FCLS) algorithm, this latter is reformulated in term of a penalized function, which reveals its suboptimality. Si...
Wang, Leana; Zhou, Yan; Liu, Cheng-hui; Zhou, Lixin; He, Yong; Pu, Yang; Nguyen, Thien An; Alfano, Robert R.
2015-03-01
The objective of this study was to find out the emission spectral fingerprints for discrimination of human colorectal and gastric cancer from normal tissue in vitro by applying native fluorescence. The native fluorescence (NFL) and Stokes shift spectra of seventy-two human cancerous and normal colorectal (colon, rectum) and gastric tissues were analyzed using three selected excitation wavelengths (e.g. 300 nm, 320 nm and 340 nm). Three distinct biomarkers, tryptophan, collagen and reduced nicotinamide adenine dinucleotide hydrate (NADH), were found in the samples of cancerous and normal tissues from eighteen subjects. The spectral profiles of tryptophan exhibited a sharp peak in cancerous colon tissues under a 300 nm excitation when compared with normal tissues. The changes in compositions of tryptophan, collagen, and NADH were found between colon cancer and normal tissues under an excitation of 300 nm by the non-negative basic biochemical component analysis (BBCA) model.
Directory of Open Access Journals (Sweden)
Jae-Gon Kim
2015-01-01
Full Text Available Lot-order assignment is to assign items in lots being processed to orders to fulfill the orders. It is usually performed periodically for meeting the due dates of orders especially in a manufacturing industry with a long production cycle time such as the semiconductor manufacturing industry. In this paper, we consider the lot-order assignment problem (LOAP with the objective of minimizing the total tardiness of the orders with distinct due dates. We show that we can solve the LOAP optimally by finding an optimal sequence for the single-machine total tardiness scheduling problem with nonnegative time-dependent processing times (SMTTSP-NNTDPT. Also, we address how the priority rules for the SMTTSP can be modified to those for the SMTTSP-NNTDPT to solve the LOAP. In computational experiments, we discuss the performances of the suggested priority rules and show the result of the proposed approach outperforms that of the commercial optimization software package.
Institute of Scientific and Technical Information of China (English)
ChenZhiping; ZhaoCaie; WangYang
2002-01-01
For the capital market satisfying standard assumptions that are widely adopted in the equilibrium analysis,a necessary and sufficient condition for the existence and uniqueness of a nonnegative equilibrium price vector that clears the mean-variance capital market with short sale allowed is derived. Moreover, the given explicit formula for the equilibrium price shows clearly the relationship between prices of assets and statistical properties of the rate of return on assets, the desired rates of return of individual investors as well as other economic quantities.The economic implication of the derived condition is briefly discussed. These results improve the available results about the equilibrium analysis of the mean-variance market.
DEFF Research Database (Denmark)
Hansen, Kristoffer Arnsfelt; Ibsen-Jensen, Rasmus; Podolskii, Vladimir V.;
2013-01-01
For matrix games we study how small nonzero probability must be used in optimal strategies. We show that for image win–lose–draw games (i.e. image matrix games) nonzero probabilities smaller than image are never needed. We also construct an explicit image win–lose game such that the unique optimal...
DEFF Research Database (Denmark)
Hansen, Kristoffer Arnsfelt; Ibsen-Jensen, Rasmus; Podolskii, Vladimir V.
2013-01-01
For matrix games we study how small nonzero probability must be used in optimal strategies. We show that for image win–lose–draw games (i.e. image matrix games) nonzero probabilities smaller than image are never needed. We also construct an explicit image win–lose game such that the unique optimal...
Seraji, H.
1987-01-01
Given a multivariable system, it is proved that the numerator matrix N(s) of the transfer function evaluated at any system pole either has unity rank or is a null matrix. It is also shown that N(s) evaluated at any transmission zero of the system has rank deficiency. Examples are given for illustration.
... may be credentialed by the American Association for Marriage and Family Therapy (AAMFT). Family therapy is often short term. ... challenging situations in a more effective way. References Marriage and family therapists: The friendly mental health professionals. American Association ...
... page: //medlineplus.gov/ency/article/000397.htm Familial hypertriglyceridemia To use the sharing features on this page, please enable JavaScript. Familial hypertriglyceridemia is a common disorder passed down through families. ...
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Your family history includes health information about you and your close relatives. Families have many factors in common, including their genes, ... as heart disease, stroke, and cancer. Having a family member with a disease raises your risk, but ...
Improved Matrix Uncertainty Selector
Rosenbaum, Mathieu
2011-01-01
We consider the regression model with observation error in the design: y=X\\theta* + e, Z=X+N. Here the random vector y in R^n and the random n*p matrix Z are observed, the n*p matrix X is unknown, N is an n*p random noise matrix, e in R^n is a random noise vector, and \\theta* is a vector of unknown parameters to be estimated. We consider the setting where the dimension p can be much larger than the sample size n and \\theta* is sparse. Because of the presence of the noise matrix N, the commonly used Lasso and Dantzig selector are unstable. An alternative procedure called the Matrix Uncertainty (MU) selector has been proposed in Rosenbaum and Tsybakov (2010) in order to account for the noise. The properties of the MU selector have been studied in Rosenbaum and Tsybakov (2010) for sparse \\theta* under the assumption that the noise matrix N is deterministic and its values are small. In this paper, we propose a modification of the MU selector when N is a random matrix with zero-mean entries having the variances th...
Eves, Howard
1980-01-01
The usefulness of matrix theory as a tool in disciplines ranging from quantum mechanics to psychometrics is widely recognized, and courses in matrix theory are increasingly a standard part of the undergraduate curriculum.This outstanding text offers an unusual introduction to matrix theory at the undergraduate level. Unlike most texts dealing with the topic, which tend to remain on an abstract level, Dr. Eves' book employs a concrete elementary approach, avoiding abstraction until the final chapter. This practical method renders the text especially accessible to students of physics, engineeri
Rheocasting Al matrix composites
Energy Technology Data Exchange (ETDEWEB)
Girot, F.A.; Albingre, L.; Quenisset, J.M.; Naslain, R.
1987-11-01
A development status account is given for the rheocasting method of Al-alloy matrix/SiC-whisker composites, which involves the incorporation and homogeneous distribution of 8-15 vol pct of whiskers through the stirring of the semisolid matrix melt while retaining sufficient fluidity for casting. Both 1-, 3-, and 6-mm fibers of Nicalon SiC and and SiC whisker reinforcements have been experimentally investigated, with attention to the characterization of the resulting microstructures and the effects of fiber-matrix interactions. A thin silica layer is found at the whisker surface. 7 references.
Mueller matrix differential decomposition.
Ortega-Quijano, Noé; Arce-Diego, José Luis
2011-05-15
We present a Mueller matrix decomposition based on the differential formulation of the Mueller calculus. The differential Mueller matrix is obtained from the macroscopic matrix through an eigenanalysis. It is subsequently resolved into the complete set of 16 differential matrices that correspond to the basic types of optical behavior for depolarizing anisotropic media. The method is successfully applied to the polarimetric analysis of several samples. The differential parameters enable one to perform an exhaustive characterization of anisotropy and depolarization. This decomposition is particularly appropriate for studying media in which several polarization effects take place simultaneously. © 2011 Optical Society of America
Directory of Open Access Journals (Sweden)
Ileno Izídio da Costa
2013-04-01
fortalecer la salud mental en atención primaria, con inversiones en la educación permanente, el establecimiento de indicadores y la integración entre los CAPS y SF.This paper discusses the experience of implementing the matrix support in mental health practice that aims to produce higher resolution and accountability for mental health situations in Family Health. The objective of the present study is to analyze the implementation of the mental health "matrix support" practice among Psychosocial Attention Centers (CAPS III and Family Health Teams. This is an action-research that had as instruments operational groups of reflection and the response to questionnaires. Groups were held with Family Health professionals, with CAPS professionals and with professionals from the two services together. Regarding the Family Health services, the result indicated difficulties in addressing mental health cases and the coexistence of sheltering and psychosocial models of care practice in their work. In relation to the CAPS, they indicated the importance of strengthening teamwork to achieve the "matrix support" practice. The results indicate the need to strengthen mental health services within the primary health care, by investing in continuing education, establishing indicators and integrating the CAPS and Family Health services.
Level crossing in random matrices: I. Random perturbation of a fixed matrix
Shapiro, B.; Zarembo, K.
2017-01-01
We consider level crossing in a matrix family H={{H}0}+λ V where H 0 is a fixed N× N matrix and V belongs to one of the standard Gaussian random matrix ensembles. We study the probability distribution of level crossing points in the complex plane of λ , for which we obtain a number of exact, asymptotic and approximate formulas.
Level Crossing in Random Matrices: I. Random perturbation of a fixed matrix
Shapiro, B
2016-01-01
We consider level crossing in a matrix family $H=H_0+\\lambda V$ where $H_0$ is a fixed $N\\times N$ matrix and $V$ belongs to one of the standard Gaussian random matrix ensembles. We study the probability distribution of level crossing points in the complex plane of $\\lambda$, for which we obtain a number of exact, asymptotic and approximate formulas.
Entanglement classification with matrix product states
Sanz, M.; Egusquiza, I. L.; di Candia, R.; Saberi, H.; Lamata, L.; Solano, E.
2016-07-01
We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families to the interaction length of Hamiltonians. In this manner, we establish a connection between entanglement classification and condensed matter models from a quantum information perspective. Moreover, we introduce a scalable nesting property for the proposed entanglement classification, in which the families for N parties carry over to the N + 1 case. Finally, using techniques from algebraic geometry, we prove that the minimal nontrivial interaction length n for any symmetric state is bounded by .
Entanglement classification with matrix product states.
Sanz, M; Egusquiza, I L; Di Candia, R; Saberi, H; Lamata, L; Solano, E
2016-07-26
We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families to the interaction length of Hamiltonians. In this manner, we establish a connection between entanglement classification and condensed matter models from a quantum information perspective. Moreover, we introduce a scalable nesting property for the proposed entanglement classification, in which the families for N parties carry over to the N + 1 case. Finally, using techniques from algebraic geometry, we prove that the minimal nontrivial interaction length n for any symmetric state is bounded by .
Indian Academy of Sciences (India)
Paul W Ayers; Mel Levy
2005-09-01
Using the constrained search and Legendre-transform formalisms, one can derive ``generalized” density-functional theories, in which the fundamental variable is either the electron pair density or the second-order reduced density matrix. In both approaches, the -representability problem is solved by the functional, and the variational principle is with respect to all pair densities (density matrices) that are nonnegative and appropriately normalized. The Legendre-transform formulation provides a lower bound on the constrained-search functional. Noting that experience in density-functional and density-matrix theories suggests that it is easier to approximate functionals than it is to approximate the set of -representable densities sheds some light on the significance of this work.
Random matrix model for disordered conductors
Indian Academy of Sciences (India)
Zafar Ahmed; Sudhir R Jain
2000-03-01
We present a random matrix ensemble where real, positive semi-deﬁnite matrix elements, , are log-normal distributed, $\\exp[-\\log^{2}(x)]$. We show that the level density varies with energy, , as 2/(1 + ) for large , in the unitary family, consistent with the expectation for disordered conductors. The two-level correlation function is studied for the unitary family and found to be largely of the universal form despite the fact that the level density has a non-compact support. The results are based on the method of orthogonal polynomials (the Stieltjes-Wigert polynomials here). An interesting random walk problem associated with the joint probability distribution of the ensuing ensemble is discussed and its connection with level dynamics is brought out. It is further proved that Dyson's Coulomb gas analogy breaks down whenever the conﬁning potential is given by a transcendental function for which there exist orthogonal polynomials.
The "Pesticide-exposure Matrix" was developed to help epidemiologists and other researchers identify the active ingredients to which people were likely exposed when their homes and gardens were treated for pests in past years.
Koehler, Wolfgang
2011-01-01
A new classical theory of gravitation within the framework of general relativity is presented. It is based on a matrix formulation of four-dimensional Riemann-spaces and uses no artificial fields or adjustable parameters. The geometrical stress-energy tensor is derived from a matrix-trace Lagrangian, which is not equivalent to the curvature scalar R. To enable a direct comparison with the Einstein-theory a tetrad formalism is utilized, which shows similarities to teleparallel gravitation theories, but uses complex tetrads. Matrix theory might solve a 27-year-old, fundamental problem of those theories (sec. 4.1). For the standard test cases (PPN scheme, Schwarzschild-solution) no differences to the Einstein-theory are found. However, the matrix theory exhibits novel, interesting vacuum solutions.
DEFF Research Database (Denmark)
Schneider, Jesper Wiborg; Borlund, Pia
2007-01-01
The present two-part article introduces matrix comparison as a formal means for evaluation purposes in informetric studies such as cocitation analysis. In the first part, the motivation behind introducing matrix comparison to informetric studies, as well as two important issues influencing...... such comparisons, matrix generation, and the composition of proximity measures, are introduced and discussed. In this second part, the authors introduce and thoroughly demonstrate two related matrix comparison techniques the Mantel test and Procrustes analysis, respectively. These techniques can compare...... important. Alternatively, or as a supplement, Procrustes analysis compares the actual ordination results without investigating the underlying proximity measures, by matching two configurations of the same objects in a multidimensional space. An advantage of the Procrustes analysis though, is the graphical...
The Matrix Organization Revisited
DEFF Research Database (Denmark)
Gattiker, Urs E.; Ulhøi, John Parm
1999-01-01
This paper gives a short overview of matrix structure and technology management. It outlines some of the characteristics and also points out that many organizations may actualy be hybrids (i.e. mix several ways of organizing to allocate resorces effectively).......This paper gives a short overview of matrix structure and technology management. It outlines some of the characteristics and also points out that many organizations may actualy be hybrids (i.e. mix several ways of organizing to allocate resorces effectively)....
Optical Coherency Matrix Tomography
2015-10-19
optics has been studied theoretically11, but has not been demonstrated experimentally heretofore. Even in the simplest case of two binary DoFs6 (e.g...coherency matrix G spanning these DoFs. This optical coherency matrix has not been measured in its entirety to date—even in the simplest case of two...dense coding, etc. CREOL, The College of Optics & Photonics, University of Central Florida, Orlando , Florida 32816, USA. Correspondence and requests
Tenreiro Machado, J. A.
2015-08-01
This paper addresses the matrix representation of dynamical systems in the perspective of fractional calculus. Fractional elements and fractional systems are interpreted under the light of the classical Cole-Cole, Davidson-Cole, and Havriliak-Negami heuristic models. Numerical simulations for an electrical circuit enlighten the results for matrix based models and high fractional orders. The conclusions clarify the distinction between fractional elements and fractional systems.
Czerwinski, Michael; Spence, Jason R
2017-01-05
Recently in Nature, Gjorevski et al. (2016) describe a fully defined synthetic hydrogel that mimics the extracellular matrix to support in vitro growth of intestinal stem cells and organoids. The hydrogel allows exquisite control over the chemical and physical in vitro niche and enables identification of regulatory properties of the matrix. Copyright © 2017 Elsevier Inc. All rights reserved.
The Matrix Organization Revisited
DEFF Research Database (Denmark)
Gattiker, Urs E.; Ulhøi, John Parm
1999-01-01
This paper gives a short overview of matrix structure and technology management. It outlines some of the characteristics and also points out that many organizations may actualy be hybrids (i.e. mix several ways of organizing to allocate resorces effectively).......This paper gives a short overview of matrix structure and technology management. It outlines some of the characteristics and also points out that many organizations may actualy be hybrids (i.e. mix several ways of organizing to allocate resorces effectively)....
Seita, John R.
2014-01-01
Family privilege is defined as "strengths and supports gained through primary caring relationships." A generation ago, the typical family included two parents and a bevy of kids living under one roof. Now, every variation of blended caregiving qualifies as family. But over the long arc of human history, a real family was a…
Seita, John R.
2014-01-01
Family privilege is defined as "strengths and supports gained through primary caring relationships." A generation ago, the typical family included two parents and a bevy of kids living under one roof. Now, every variation of blended caregiving qualifies as family. But over the long arc of human history, a real family was a…
Matrix metalloproteinases (MMPs) and trophoblast invasion
Institute of Scientific and Technical Information of China (English)
LI Jing; ZHAO Tianfu; DUAN Enkui
2005-01-01
MMPs and their natural tissue inhibitors TIMPs are crucial in coordinated breakdown and remodeling of the extracellular matrix (ECM) in physiological and pathological situations. Placentation is a key event of pregnancy in which MMPs/TIMPs system plays important roles in regulating the extravillus cytotrophoblast (EVTs) invasion. This paper focuses on expression patterns and regulatory mechanisms of MMPs/TIMPs family members during the process of placentation. Their implications in curing pregnancy-related diseases are also discussed.
Song, Qiang; Liu, Fang; Cao, Jinde; Yu, Wenwu
2013-12-01
This paper considers the leader-following consensus problem for multiagent systems with inherent nonlinear dynamics. Some M-matrix strategies are developed to address several challenging issues in the pinning control of multiagent systems by using algebraic graph theory and the properties of nonnegative matrices. It is shown that second-order leader-following consensus in a nonlinear multiagent system can be reached if the virtual leader has a directed path to every follower and a derived quantity is greater than a positive threshold. In particular, this paper analytically proves that leader-following consensus may be easier to be achieved by pinning more agents or increasing the pinning feedback gains. A selective pinning scheme is then proposed for nonlinear multiagent systems with directed network topologies. Numerical results are given to verify the theoretical analysis.
Redesigning Triangular Dense Matrix Computations on GPUs
Charara, Ali
2016-08-09
A new implementation of the triangular matrix-matrix multiplication (TRMM) and the triangular solve (TRSM) kernels are described on GPU hardware accelerators. Although part of the Level 3 BLAS family, these highly computationally intensive kernels fail to achieve the percentage of the theoretical peak performance on GPUs that one would expect when running kernels with similar surface-to-volume ratio on hardware accelerators, i.e., the standard matrix-matrix multiplication (GEMM). The authors propose adopting a recursive formulation, which enriches the TRMM and TRSM inner structures with GEMM calls and, therefore, reduces memory traffic while increasing the level of concurrency. The new implementation enables efficient use of the GPU memory hierarchy and mitigates the latency overhead, to run at the speed of the higher cache levels. Performance comparisons show up to eightfold and twofold speedups for large dense matrix sizes, against the existing state-of-the-art TRMM and TRSM implementations from NVIDIA cuBLAS, respectively, across various GPU generations. Once integrated into high-level Cholesky-based dense linear algebra algorithms, the performance impact on the overall applications demonstrates up to fourfold and twofold speedups, against the equivalent native implementations, linked with cuBLAS TRMM and TRSM kernels, respectively. The new TRMM/TRSM kernel implementations are part of the open-source KBLAS software library (http://ecrc.kaust.edu.sa/Pages/Res-kblas.aspx) and are lined up for integration into the NVIDIA cuBLAS library in the upcoming v8.0 release.
Bhatia, Rajendra
2013-01-01
This book is an outcome of the Indo-French Workshop on Matrix Information Geometries (MIG): Applications in Sensor and Cognitive Systems Engineering, which was held in Ecole Polytechnique and Thales Research and Technology Center, Palaiseau, France, in February 23-25, 2011. The workshop was generously funded by the Indo-French Centre for the Promotion of Advanced Research (IFCPAR). During the event, 22 renowned invited french or indian speakers gave lectures on their areas of expertise within the field of matrix analysis or processing. From these talks, a total of 17 original contribution or state-of-the-art chapters have been assembled in this volume. All articles were thoroughly peer-reviewed and improved, according to the suggestions of the international referees. The 17 contributions presented are organized in three parts: (1) State-of-the-art surveys & original matrix theory work, (2) Advanced matrix theory for radar processing, and (3) Matrix-based signal processing applications.
Zhang, Shan-chuan; Kern, Matthias
2009-01-01
Dentin matrix metalloproteinases (MMPs) are a family of host-derived proteolytic enzymes trapped within mineralized dentin matrix, which have the ability to hydrolyze the organic matrix of demineralized dentin. After bonding with resins to dentin there are usually some exposed collagen fibrils at the bottom of the hybrid layer owing to imperfect resin impregnation of the demineralized dentin matrix. Exposed collagen fibrils might be affected by MMPs inducing hydrolytic degradation, which migh...
Chen, Hai-yang; Teng, Yan-guo; Wang, Jin-sheng
2012-01-01
In this study, sources of polycyclic aromatic hydrocarbons (PAHs) found in surface sediments of the Rizhao coastal area (China) were apportioned using diagnostic ratios and factor analysis with nonnegative constraints (FA-NNC). Bivariate plots of selected diagnostic ratios showed that the sources of PAHs identified in surface sediments seemed to be mixed sources dominated by petroleum-related. Literature PAH source profiles were modified based on the first-order degradation reaction in the atmosphere and sediments, and were considered as comparison for source identification. Five significant factors were determined with the diagnostic tools including coefficient of determination, cumulative percent variance and Exner function. By visually comparing PAH patterns and from the sum of squares of differences between modeled and modified literature PAH profiles, the potential sources were apportioned with the FA-NNC. The main contribution sources of PAHs originated from diesel engine (27.22%), followed by traffic emission (25.03%), gasoline engine (18.95%), coal power plant (14.77%) and coal residential (14.03%). Energy consumption was the predominant reason for PAH pollution in that region.
Directory of Open Access Journals (Sweden)
J. Mukerji
1993-10-01
Full Text Available The present state of the knowledge of ceramic-matrix composites have been reviewed. The fracture toughness of present structural ceramics are not enough to permit design of high performance machines with ceramic parts. They also fail by catastrophic brittle fracture. It is generally believed that further improvement of fracture toughness is only possible by making composites of ceramics with ceramic fibre, particulate or platelets. Only ceramic-matrix composites capable of working above 1000 degree centigrade has been dealt with keeping reinforced plastics and metal-reinforced ceramics outside the purview. The author has discussed the basic mechanisms of toughening and fabrication of composites and the difficulties involved. Properties of available fibres and whiskers have been given. The best results obtained so far have been indicated. The limitations of improvement in properties of ceramic-matrix composites have been discussed.
Energy Technology Data Exchange (ETDEWEB)
Pan, Feng [Los Alamos National Laboratory; Kasiviswanathan, Shiva [Los Alamos National Laboratory
2010-01-01
In the matrix interdiction problem, a real-valued matrix and an integer k is given. The objective is to remove k columns such that the sum over all rows of the maximum entry in each row is minimized. This combinatorial problem is closely related to bipartite network interdiction problem which can be applied to prioritize the border checkpoints in order to minimize the probability that an adversary can successfully cross the border. After introducing the matrix interdiction problem, we will prove the problem is NP-hard, and even NP-hard to approximate with an additive n{gamma} factor for a fixed constant {gamma}. We also present an algorithm for this problem that achieves a factor of (n-k) mUltiplicative approximation ratio.
Finite Temperature Matrix Theory
Meana, M L; Peñalba, J P; Meana, Marco Laucelli; Peñalba, Jesús Puente
1998-01-01
We present the way the Lorentz invariant canonical partition function for Matrix Theory as a light-cone formulation of M-theory can be computed. We explicitly show how when the eleventh dimension is decompactified, the N=1 eleven dimensional SUGRA partition function appears. From this particular analysis we also clarify the question about the discernibility problem when making statistics with supergravitons (the N! problem) in Matrix black hole configurations. We also provide a high temperature expansion which captures some structure of the canonical partition function when interactions amongst D-particles are on. The connection with the semi-classical computations thermalizing the open superstrings attached to a D-particle is also clarified through a Born-Oppenheimer approximation. Some ideas about how Matrix Theory would describe the complementary degrees of freedom of the massless content of eleven dimensional SUGRA are also discussed.
Matrixed business support comparison study.
Energy Technology Data Exchange (ETDEWEB)
Parsons, Josh D.
2004-11-01
The Matrixed Business Support Comparison Study reviewed the current matrixed Chief Financial Officer (CFO) division staff models at Sandia National Laboratories. There were two primary drivers of this analysis: (1) the increasing number of financial staff matrixed to mission customers and (2) the desire to further understand the matrix process and the opportunities and challenges it creates.
Aoki, H; Kawai, H; Kitazawa, Y; Tada, T; Tsuchiya, A
1999-01-01
We review our proposal for a constructive definition of superstring, type IIB matrix model. The IIB matrix model is a manifestly covariant model for space-time and matter which possesses N=2 supersymmetry in ten dimensions. We refine our arguments to reproduce string perturbation theory based on the loop equations. We emphasize that the space-time is dynamically determined from the eigenvalue distributions of the matrices. We also explain how matter, gauge fields and gravitation appear as fluctuations around dynamically determined space-time.
Kitazawa, Y; Saito, O; Kitazawa, Yoshihisa; Mizoguchi, Shun'ya; Saito, Osamu
2006-01-01
We study the zero-dimensional reduced model of D=6 pure super Yang-Mills theory and argue that the large N limit describes the (2,0) Little String Theory. The one-loop effective action shows that the force exerted between two diagonal blocks of matrices behaves as 1/r^4, implying a six-dimensional spacetime. We also observe that it is due to non-gravitational interactions. We construct wave functions and vertex operators which realize the D=6, (2,0) tensor representation. We also comment on other "little" analogues of the IIB matrix model and Matrix Theory with less supercharges.
Hohn, Franz E
2012-01-01
This complete and coherent exposition, complemented by numerous illustrative examples, offers readers a text that can teach by itself. Fully rigorous in its treatment, it offers a mathematically sound sequencing of topics. The work starts with the most basic laws of matrix algebra and progresses to the sweep-out process for obtaining the complete solution of any given system of linear equations - homogeneous or nonhomogeneous - and the role of matrix algebra in the presentation of useful geometric ideas, techniques, and terminology.Other subjects include the complete treatment of the structur
Rheocasting Al Matrix Composites
Girot, F. A.; Albingre, L.; Quenisset, J. M.; Naslain, R.
1987-11-01
Aluminum alloy matrix composites reinforced by SiC short fibers (or whiskers) can be prepared by rheocasting, a process which consists of the incorporation and homogeneous distribution of the reinforcement by stirring within a semi-solid alloy. Using this technique, composites containing fiber volume fractions in the range of 8-15%, have been obtained for various fibers lengths (i.e., 1 mm, 3 mm and 6 mm for SiC fibers). This paper attempts to delineate the best compocasting conditions for aluminum matrix composites reinforced by short SiC (e.g Nicalon) or SiC whiskers (e.g., Tokamax) and characterize the resulting microstructures.
Frahm, K M
2016-01-01
Using parallels with the quantum scattering theory, developed for processes in nuclear and mesoscopic physics and quantum chaos, we construct a reduced Google matrix $G_R$ which describes the properties and interactions of a certain subset of selected nodes belonging to a much larger directed network. The matrix $G_R$ takes into account effective interactions between subset nodes by all their indirect links via the whole network. We argue that this approach gives new possibilities to analyze effective interactions in a group of nodes embedded in a large directed networks. Possible efficient numerical methods for the practical computation of $G_R$ are also described.
Density matrix perturbation theory.
Niklasson, Anders M N; Challacombe, Matt
2004-05-14
An orbital-free quantum perturbation theory is proposed. It gives the response of the density matrix upon variation of the Hamiltonian by quadratically convergent recursions based on perturbed projections. The technique allows treatment of embedded quantum subsystems with a computational cost scaling linearly with the size of the perturbed region, O(N(pert.)), and as O(1) with the total system size. The method allows efficient high order perturbation expansions, as demonstrated with an example involving a 10th order expansion. Density matrix analogs of Wigner's 2n+1 rule are also presented.
Energy Technology Data Exchange (ETDEWEB)
Brown, T.W.
2010-11-15
The same complex matrix model calculates both tachyon scattering for the c=1 non-critical string at the self-dual radius and certain correlation functions of half-BPS operators in N=4 super- Yang-Mills. It is dual to another complex matrix model where the couplings of the first model are encoded in the Kontsevich-like variables of the second. The duality between the theories is mirrored by the duality of their Feynman diagrams. Analogously to the Hermitian Kontsevich- Penner model, the correlation functions of the second model can be written as sums over discrete points in subspaces of the moduli space of punctured Riemann surfaces. (orig.)
Noncommmutative Batalin-Vilkovisky geometry and Matrix integrals
Barannikov, Serguei
2009-01-01
We associate the new type of supersymmetric matrix models with any solution to the quantum master equation of the noncommutative Batalin-Vilkovisky geometry. The asymptotic expansion of the matrix integrals gives homology classes in the Kontsevich compactification of the moduli spaces, which we associated with the solutions to the quantum master equation in our previous paper. We associate with the queer matrix superalgebra equipped with an odd differentiation, whose square is nonzero, the family of cohomology classes of the compactification. This family is the generating function for the products of the tautological classes. The simplest example of the matrix integrals in the case of dimension zero is a supersymmetric extenstion of the Kontsevich model of 2-dimensional gravity.
W.W. de Herder (Wouter)
2012-01-01
textabstractFamilial GH-secreting tumors are seen in association with three separate hereditary clinical syndromes: multiple endocrine neoplasia type 1, Carney complex, and familial isolated pituitary adenomas.
Directory of Open Access Journals (Sweden)
Wouter W. de Herder
2012-01-01
Full Text Available Familial GH-secreting tumors are seen in association with three separate hereditary clinical syndromes: multiple endocrine neoplasia type 1, Carney complex, and familial isolated pituitary adenomas.
Jedele, K B; Michels, V V
1991-05-01
Urticaria in response to various physical stimuli has been reported in sporadic and familial patterns. The most common of these physical urticarias, dermographism, is a localized urticarial response to stroking or scratching of the skin and has not been reported previously to be familial. A four-generation family with dermographism, probably inherited as an autosomal dominant trait, is presented along with a discussion of sporadic dermographism and other types of familial physical urticarias.
On the number of a SDRs of a valued (t,n)-family
He, Dawei
2010-01-01
A system of distinct representatives (SDR) of a family $F = (A_1, \\cdots, A_n)$ is a sequence $(x_1, \\cdots, x_n)$ of $n$ distinct elements with $x_i \\in A_i$ for $1 \\le i \\le n$. Let $N(F)$ denote the number of SDRs of a family $F$; two SDRs are considered distinct if they are different in at least one component. For a nonnegative integer $t$, a family $F=(A_1,\\cdots,A_n)$ is called a $(t,n)$-family if the union of any $k\\ge 1$ sets in the family contains at least $k+t$ elements. The famous Hall's Theorem says that $N(F)\\ge 1$ if and only if $F$ is a $(0,n)$-family. Denote by $M(t,n)$ the minimum number of SDRs in a $(t,n)$-family. The problem of determining $M(t,n)$ and those families containing exactly $M(t,n)$ SDRs was first raised by Chang [European J. Combin.{\\bf 10}(1989), 231-234]. He solved the cases when $0\\le t\\le 2$ and gave a conjecture for $t\\ge 3$. In this paper, we solve the conjecture. In fact, we get a more general result for so-called valued $(t,n)$-family.
Radiative fermion mass matrix generation in supersymmetric models
Energy Technology Data Exchange (ETDEWEB)
Papantonopoulos, E.; Zoupanos, G.
1984-01-01
Supersymmetric SU(2)sub(L)xU(1) horizontal models are studied. The non-renormalisation theorems of sypersymmetry are used to make the mass generation and flavour mixing natural. For three families, the fermion mass matrix generation mechanism is studied as a radiative effect due to horizontal interactions, using various representations of the gauge horizontal groups SU(2)sub(H) and SU(3)sub(H). An attractive possibility leading to a realistic mass matrix is found.
Removing non-stationary noise in spectrum sensing using matrix factorization
van Bloem, Jan-Willem; Schiphorst, Roel; Slump, Cornelis H.
2013-12-01
Spectrum sensing is key to many applications like dynamic spectrum access (DSA) systems or telecom regulators who need to measure utilization of frequency bands. The International Telecommunication Union (ITU) recommends a 10 dB threshold above the noise to decide whether a channel is occupied or not. However, radio frequency (RF) receiver front-ends are non-ideal. This means that the obtained data is distorted with noise and imperfections from the analog front-end. As part of the front-end the automatic gain control (AGC) circuitry mainly affects the sensing performance as strong adjacent signals lift the noise level. To enhance the performance of spectrum sensing significantly we focus in this article on techniques to remove the noise caused by the AGC from the sensing data. In order to do this we have applied matrix factorization techniques, i.e., SVD (singular value decomposition) and NMF (non-negative matrix factorization), which enables signal space analysis. In addition, we use live measurement results to verify the performance and to remove the effects of the AGC from the sensing data using above mentioned techniques, i.e., applied on block-wise available spectrum data. In this article it is shown that the occupancy in the industrial, scientific and medical (ISM) band, obtained by using energy detection (ITU recommended threshold), can be an overestimation of spectrum usage by 60%.
Directory of Open Access Journals (Sweden)
Behnaz Ghoraani
2009-01-01
Full Text Available The number of people affected by speech problems is increasing as the modern world places increasing demands on the human voice via mobile telephones, voice recognition software, and interpersonal verbal communications. In this paper, we propose a novel methodology for automatic pattern classification of pathological voices. The main contribution of this paper is extraction of meaningful and unique features using Adaptive time-frequency distribution (TFD and nonnegative matrix factorization (NMF. We construct Adaptive TFD as an effective signal analysis domain to dynamically track the nonstationarity in the speech and utilize NMF as a matrix decomposition (MD technique to quantify the constructed TFD. The proposed method extracts meaningful and unique features from the joint TFD of the speech, and automatically identifies and measures the abnormality of the signal. Depending on the abnormality measure of each signal, we classify the signal into normal or pathological. The proposed method is applied on the Massachusetts Eye and Ear Infirmary (MEEI voice disorders database which consists of 161 pathological and 51 normal speakers, and an overall classification accuracy of 98.6% was achieved.
DEFF Research Database (Denmark)
Frandsen, Gudmund Skovbjerg; Frandsen, Peter Frands
2009-01-01
We consider maintaining information about the rank of a matrix under changes of the entries. For n×n matrices, we show an upper bound of O(n1.575) arithmetic operations and a lower bound of Ω(n) arithmetic operations per element change. The upper bound is valid when changing up to O(n0.575) entri...... closed fields. The upper bound for element updates uses fast rectangular matrix multiplication, and the lower bound involves further development of an earlier technique for proving lower bounds for dynamic computation of rational functions.......We consider maintaining information about the rank of a matrix under changes of the entries. For n×n matrices, we show an upper bound of O(n1.575) arithmetic operations and a lower bound of Ω(n) arithmetic operations per element change. The upper bound is valid when changing up to O(n0.575) entries...... in a single column of the matrix. We also give an algorithm that maintains the rank using O(n2) arithmetic operations per rank one update. These bounds appear to be the first nontrivial bounds for the problem. The upper bounds are valid for arbitrary fields, whereas the lower bound is valid for algebraically...
Empirical codon substitution matrix
Directory of Open Access Journals (Sweden)
Gonnet Gaston H
2005-06-01
Full Text Available Abstract Background Codon substitution probabilities are used in many types of molecular evolution studies such as determining Ka/Ks ratios, creating ancestral DNA sequences or aligning coding DNA. Until the recent dramatic increase in genomic data enabled construction of empirical matrices, researchers relied on parameterized models of codon evolution. Here we present the first empirical codon substitution matrix entirely built from alignments of coding sequences from vertebrate DNA and thus provide an alternative to parameterized models of codon evolution. Results A set of 17,502 alignments of orthologous sequences from five vertebrate genomes yielded 8.3 million aligned codons from which the number of substitutions between codons were counted. From this data, both a probability matrix and a matrix of similarity scores were computed. They are 64 × 64 matrices describing the substitutions between all codons. Substitutions from sense codons to stop codons are not considered, resulting in block diagonal matrices consisting of 61 × 61 entries for the sense codons and 3 × 3 entries for the stop codons. Conclusion The amount of genomic data currently available allowed for the construction of an empirical codon substitution matrix. However, more sequence data is still needed to construct matrices from different subsets of DNA, specific to kingdoms, evolutionary distance or different amount of synonymous change. Codon mutation matrices have advantages for alignments up to medium evolutionary distances and for usages that require DNA such as ancestral reconstruction of DNA sequences and the calculation of Ka/Ks ratios.
Matrix Embedded Organic Synthesis
Kamakolanu, U. G.; Freund, F. T.
2016-05-01
In the matrix of minerals such as olivine, a redox reaction of the low-z elements occurs. Oxygen is oxidized to the peroxy state while the low-Z-elements become chemically reduced. We assign them a formula [CxHyOzNiSj]n- and call them proto-organics.
Permutation Centralizer Algebras and Multi-Matrix Invariants
Mattioli, Paolo
2016-01-01
We introduce a class of permutation centralizer algebras which underly the combinatorics of multi-matrix gauge invariant observables. One family of such non-commutative algebras is parametrised by two integers. Its Wedderburn-Artin decomposition explains the counting of restricted Schur operators, which were introduced in the physics literature to describe open strings attached to giant gravitons and were subsequently used to diagonalize the Gaussian inner product for gauge invariants of 2-matrix models. The structure of the algebra, notably its dimension, its centre and its maximally commuting sub-algebra, is related to Littlewood-Richardson numbers for composing Young diagrams. It gives a precise characterization of the minimal set of charges needed to distinguish arbitrary matrix gauge invariants, which are related to enhanced symmetries in gauge theory. The algebra also gives a star product for matrix invariants. The centre of the algebra allows efficient computation of a sector of multi-matrix correlator...
Matrix metalloproteinases in wound repair (review).
Ravanti, L; Kähäri, V M
2000-10-01
Wound repair is initiated with the aggregation of platelets, formation of a fibrin clot, and release of growth factors from the activated coagulation pathways, injured cells, platelets, and extracellular matrix (ECM), followed by migration of inflammatory cells to the wound site. Thereafter, keratinocytes migrate over the wound, angiogenesis is initiated, and fibroblasts deposit and remodel the granulation tissue. Cell migration, angiogenesis, degradation of provisional matrix, and remodeling of newly formed granulation tissue, all require controlled degradation of the ECM. Disturbance in the balance between ECM production and degradation leads to formation of chronic ulcers with excessive ECM degradation, or to fibrosis, for example hypertrophic scars or keloids characterized by excessive accumulation of ECM components. Matrix metalloproteinases (MMPs) are a family of zinc-dependent endopeptidases, which as a group can degrade essentially all ECM components. So far, 20 members of the human MMP family have been identified. Based on their structure and substrate specificity, they can be divided into subgroups of collagenases, stromelysins, stromelysin-like MMPs, gelatinases, membrane-type MMPs (MT-MMPs), and other MMPs. In this review, the role of MMPs in normal wound repair as well as in chronic ulcers is discussed. In addition, the role of signaling pathways, in particular, mitogen-activated protein kinases (MAPKs) in regulating MMP expression is discussed as possible therapeutical targets for wound healing disorders.
Directory of Open Access Journals (Sweden)
Maria Salete Bessa Jorge
2012-01-01
Full Text Available OBJECTIVE: To analyze how the matrix support in mental health contributes to the production of comprehensive care with an emphasis on the interrelationships between worker / user / family. METHODS: Research with a qualitative approach that used the techniques of interview, focus group and systematic observation. Data analysis was based on critical hermeneutics. RESULTS: Matrix support in mental health assumes pedagogical and technical assistance dimensions, which favor the interaction between primary care teams and specialist teams of the Center for Psychosocial Care (CAPS, ensuring territorially-based care, with interaction of different knowledge and practices. It further contributes to the redirection of the flow of users in seeking care for their health needs, articulating the levels of health care. The worker / user / family interrelationships are closer and allow better accommodation to the demands and bond of the team with the user and his family. However, difficulties such as consolidation of the matrix support, and the predominance of biomedical practice are noted. CONCLUSION: The matrix support contributes an expansion of the spaces of mental health care in the territory, opening living spaces, creation in the reaction of the worker / user / family, and thus configuring itself as a device for the production of integrated care.OBJETIVO: Analizar cómo el apoyo matricial en salud mental contribuye con la producción del cuidado integral con énfasis en las interrelaciones entre trabajador/usuario/familia. MÉTODOS: Investigación con abordaje cualitativo en el que se utilizó las técnicas de entrevista, grupo focal y observación sistemática. El análisis de los datos se fundamentó en la hermenéutica crítica. RESULTADOS: El apoyo matricial en salud mental asume dimensiones pedagógicas y técnico-asistenciales, que favorecen la interacción entre equipos de la atención básica y equipos especializados del Centro de Atenci
Dijkgraaf, Robbert; Verlinde, Erik; Verlinde, Herman
1997-02-01
Via compactification on a circle, the matrix mode] of M-theory proposed by Banks et a]. suggests a concrete identification between the large N limit of two-dimensional N = 8 supersymmetric Yang-Mills theory and type IIA string theory. In this paper we collect evidence that supports this identification. We explicitly identify the perturbative string states and their interactions, and describe the appearance of D-particle and D-membrane states.
Energy Technology Data Exchange (ETDEWEB)
Dijkgraaf, R. [Amsterdam Univ. (Netherlands). Dept. of Mathematics; Verlinde, E. [TH-Division, CERN, CH-1211 Geneva 23 (Switzerland)]|[Institute for Theoretical Physics, Universtity of Utrecht, 3508 TA Utrecht (Netherlands); Verlinde, H. [Institute for Theoretical Physics, University of Amsterdam, 1018 XE Amsterdam (Netherlands)
1997-09-01
Via compactification on a circle, the matrix model of M-theory proposed by Banks et al. suggests a concrete identification between the large N limit of two-dimensional N=8 supersymmetric Yang-Mills theory and type IIA string theory. In this paper we collect evidence that supports this identification. We explicitly identify the perturbative string states and their interactions, and describe the appearance of D-particle and D-membrane states. (orig.).
Dijkgraaf, R; Verlinde, Herman L
1997-01-01
Via compactification on a circle, the matrix model of M-theory proposed by Banks et al suggests a concrete identification between the large N limit of two-dimensional N=8 supersymmetric Yang-Mills theory and type IIA string theory. In this paper we collect evidence that supports this identification. We explicitly identify the perturbative string states and their interactions, and describe the appearance of D-particle and D-membrane states.
Felder, G; Felder, Giovanni; Riser, Roman
2004-01-01
We study a class of holomorphic matrix models. The integrals are taken over middle dimensional cycles in the space of complex square matrices. As the size of the matrices tends to infinity, the distribution of eigenvalues is given by a measure with support on a collection of arcs in the complex planes. We show that the arcs are level sets of the imaginary part of a hyperelliptic integral connecting branch points.
Matrix groups for undergraduates
Tapp, Kristopher
2016-01-01
Matrix groups touch an enormous spectrum of the mathematical arena. This textbook brings them into the undergraduate curriculum. It makes an excellent one-semester course for students familiar with linear and abstract algebra and prepares them for a graduate course on Lie groups. Matrix Groups for Undergraduates is concrete and example-driven, with geometric motivation and rigorous proofs. The story begins and ends with the rotations of a globe. In between, the author combines rigor and intuition to describe the basic objects of Lie theory: Lie algebras, matrix exponentiation, Lie brackets, maximal tori, homogeneous spaces, and roots. This second edition includes two new chapters that allow for an easier transition to the general theory of Lie groups. From reviews of the First Edition: This book could be used as an excellent textbook for a one semester course at university and it will prepare students for a graduate course on Lie groups, Lie algebras, etc. … The book combines an intuitive style of writing w...
Directory of Open Access Journals (Sweden)
Pradeep K. Rohatgi
1993-10-01
Full Text Available This paper reviews the world wide upsurge in metal matrix composite research and development activities with particular emphasis on cast metal-matrix particulate composites. Extensive applications of cast aluminium alloy MMCs in day-to-day use in transportation as well as durable good industries are expected to advance rapidly in the next decade. The potential for extensive application of cast composites is very large in India, especially in the areas of transportation, energy and electromechanical machinery; the extensive use of composites can lead to large savings in materials and energy, and in several instances, reduce environmental pollution. It is important that engineering education and short-term courses be organized to bring MMCs to the attention of students and engineering industry leaders. India already has excellent infrastructure for development of composites, and has a long track record of world class research in cast metal matrix particulate composites. It is now necessary to catalyze prototype and regular production of selected composite components, and get them used in different sectors, especially railways, cars, trucks, buses, scooters and other electromechanical machinery. This will require suitable policies backed up by funding to bring together the first rate talent in cast composites which already exists in India, to form viable development groups followed by setting up of production plants involving the process engineering capability already available within the country. On the longer term, cast composites should be developed for use in energy generation equipment, electronic packaging aerospace systems, and smart structures.
DEFF Research Database (Denmark)
Ernst, Erik
2001-01-01
safety and flexibility at the level of multi-object systems. We are granted the flexibility of using different families of kinds of objects, and we are guaranteed the safety of the combination. This paper highlights the inability of traditional polymorphism to handle multiple objects, and presents family...... polymorphism as a way to overcome this problem. Family polymorphism has been implemented in the programming language gbeta, a generalized version of Beta, and the source code of this implementation is available under GPL....
Institute of Scientific and Technical Information of China (English)
无
2012-01-01
Everyone has a family.We live in it and feel very warm.There are three persons in my family,my mother,father and I.We live together very happily and there are many interesting stories about my family. My father is a hard-working man.He works as a doctor.He always tries his best to help every,patient and make patients comfortable.But sonetimes he works so hard
DEFF Research Database (Denmark)
Ernst, Erik
2001-01-01
safety and flexibility at the level of multi-object systems. We are granted the flexibility of using different families of kinds of objects, and we are guaranteed the safety of the combination. This paper highlights the inability of traditional polymorphism to handle multiple objects, and presents family...... polymorphism as a way to overcome this problem. Family polymorphism has been implemented in the programming language gbeta, a generalized version of Beta, and the source code of this implementation is available under GPL....
DEFF Research Database (Denmark)
Sehested, Caroline
2012-01-01
I Projekt familielæsning, der er et samarbejde mellem Nationalt Videncenter for Læsning og Hillerød Bibliotek, arbejder vi med at få kontakt til de familier, som biblioteket ellers aldrig ser som brugere og dermed også de børn, der vokser op i familier, for hvem bøger og oplæsningssituationer ikke...... er en selvfølgelig del af barndommen. Det, vi vil undersøge og ønsker at være med til at udvikle hos disse familier, er det, man kan kalde family literacy....
Matrix Theory of Small Oscillations
Chavda, L. K.
1978-01-01
A complete matrix formulation of the theory of small oscillations is presented. Simple analytic solutions involving matrix functions are found which clearly exhibit the transients, the damping factors, the Breit-Wigner form for resonances, etc. (BB)
Matrix Completions and Chordal Graphs
Institute of Scientific and Technical Information of China (English)
Kenneth John HARRISON
2003-01-01
In a matrix-completion problem the aim is to specifiy the missing entries of a matrix inorder to produce a matrix with particular properties. In this paper we survey results concerning matrix-completion problems where we look for completions of various types for partial matrices supported ona given pattern. We see that thc existence of completions of the required type often depends on thechordal properties of graphs associated with the pattern.
DEFF Research Database (Denmark)
Yang, Yukay
I consider multivariate (vector) time series models in which the error covariance matrix may be time-varying. I derive a test of constancy of the error covariance matrix against the alternative that the covariance matrix changes over time. I design a new family of Lagrange-multiplier tests against...
THE GENERALIZED POLARIZATION SCATTERING MATRIX
the Least Square Best Estimate of the Generalized Polarization matrix from a set of measurements is then developed. It is shown that the Faraday...matrix data. It is then shown that the Least Square Best Estimate of the orientation angle of a symmetric target is also determinable from Faraday rotation contaminated short pulse monostatic polarization matrix data.
... easier for both parents to combine careers with family life. The general stress level is lower because there often are fewer ... can help replace the missing ties. For many families, religious congregational ... such as youth and neighborhood activity centers also can fulfill these ...
Wendt, Lisiane Dilli
2016-06-14
Platystomatidae (Signal Flies) are one of the largest families of Tephritoidea, with about 1200 species and four subfamilies, worldwide distributed. However, Platystomatidae are not well represented in the New World, and in the Neotropical Region only four genera and 26 species, belonging to Platystomatinae, are recorded. The family is a group understudied in Colombia and only one species is recorded to the country.
Effect of Non-negative Chinese Affective Pictures on Patients with Chronic Pain%非负性中国情感图片对慢性疼痛患者的影响
Institute of Scientific and Technical Information of China (English)
王婷婷; 史婷奇
2016-01-01
Objective To study the effect of non-negative Chinese affective pictures on the pain of patients with chronic pain. Methods A total of 77 hospitalized patients with chronic pain, according to admission number, were divided into intervention group and control group. In intervention group, routine nursing for pain and non-negative pictures from CAPS (non-negative Chinese affective picture system) were conducted and applied but in control group only routine nursing for pain was performed. NRS (numerical rating scale) was used for pain assessment in two groups. Results After intervention for 6 times, the NRS score of intervention group was lower than that of control group and the difference indicated statistical significance ( P<0.05). Conclusion Non-negative Chinese affective pictures can reduce the pain of patients.%目的：研究非负性中国情感图片对慢性疼痛患者疼痛的影响。方法将77例慢性疼痛的住院患者按住院号单双号分为干预组和对照组。干预组采用疼痛科护理常规和非负性中国情感图片疗法，对照组采用疼痛科护理常规，采集2组疼痛数字评分法（Numerical Ratingscale，NRS）的结果并进行2组干预前后疼痛数字评分法得分比较。结果干预6次后，干预组的疼痛数字评分低于对照组，差异有统计学意义（P<0.05）。结论非负性中国情感图片可以减轻患者疼痛程度。
The cellulose resource matrix.
Keijsers, Edwin R P; Yılmaz, Gülden; van Dam, Jan E G
2013-03-01
The emerging biobased economy is causing shifts from mineral fossil oil based resources towards renewable resources. Because of market mechanisms, current and new industries utilising renewable commodities, will attempt to secure their supply of resources. Cellulose is among these commodities, where large scale competition can be expected and already is observed for the traditional industries such as the paper industry. Cellulose and lignocellulosic raw materials (like wood and non-wood fibre crops) are being utilised in many industrial sectors. Due to the initiated transition towards biobased economy, these raw materials are intensively investigated also for new applications such as 2nd generation biofuels and 'green' chemicals and materials production (Clark, 2007; Lange, 2007; Petrus & Noordermeer, 2006; Ragauskas et al., 2006; Regalbuto, 2009). As lignocellulosic raw materials are available in variable quantities and qualities, unnecessary competition can be avoided via the choice of suitable raw materials for a target application. For example, utilisation of cellulose as carbohydrate source for ethanol production (Kabir Kazi et al., 2010) avoids the discussed competition with easier digestible carbohydrates (sugars, starch) deprived from the food supply chain. Also for cellulose use as a biopolymer several different competing markets can be distinguished. It is clear that these applications and markets will be influenced by large volume shifts. The world will have to reckon with the increase of competition and feedstock shortage (land use/biodiversity) (van Dam, de Klerk-Engels, Struik, & Rabbinge, 2005). It is of interest - in the context of sustainable development of the bioeconomy - to categorize the already available and emerging lignocellulosic resources in a matrix structure. When composing such "cellulose resource matrix" attention should be given to the quality aspects as well as to the available quantities and practical possibilities of processing the
Matrix string partition function
Kostov, Ivan K; Kostov, Ivan K.; Vanhove, Pierre
1998-01-01
We evaluate quasiclassically the Ramond partition function of Euclidean D=10 U(N) super Yang-Mills theory reduced to a two-dimensional torus. The result can be interpreted in terms of free strings wrapping the space-time torus, as expected from the point of view of Matrix string theory. We demonstrate that, when extrapolated to the ultraviolet limit (small area of the torus), the quasiclassical expressions reproduce exactly the recently obtained expression for the partition of the completely reduced SYM theory, including the overall numerical factor. This is an evidence that our quasiclassical calculation might be exact.
Eisenman, Richard L
2005-01-01
This outstanding text and reference applies matrix ideas to vector methods, using physical ideas to illustrate and motivate mathematical concepts but employing a mathematical continuity of development rather than a physical approach. The author, who taught at the U.S. Air Force Academy, dispenses with the artificial barrier between vectors and matrices--and more generally, between pure and applied mathematics.Motivated examples introduce each idea, with interpretations of physical, algebraic, and geometric contexts, in addition to generalizations to theorems that reflect the essential structur
Deift, Percy
2009-01-01
This book features a unified derivation of the mathematical theory of the three classical types of invariant random matrix ensembles-orthogonal, unitary, and symplectic. The authors follow the approach of Tracy and Widom, but the exposition here contains a substantial amount of additional material, in particular, facts from functional analysis and the theory of Pfaffians. The main result in the book is a proof of universality for orthogonal and symplectic ensembles corresponding to generalized Gaussian type weights following the authors' prior work. New, quantitative error estimates are derive
Supported Molecular Matrix Electrophoresis.
Matsuno, Yu-Ki; Kameyama, Akihiko
2015-01-01
Mucins are difficult to separate using conventional gel electrophoresis methods such as SDS-PAGE and agarose gel electrophoresis, owing to their large size and heterogeneity. On the other hand, cellulose acetate membrane electrophoresis can separate these molecules, but is not compatible with glycan analysis. Here, we describe a novel membrane electrophoresis technique, termed "supported molecular matrix electrophoresis" (SMME), in which a porous polyvinylidene difluoride (PVDF) membrane filter is used to achieve separation. This description includes the separation, visualization, and glycan analysis of mucins with the SMME technique.
Matrix algebra for linear models
Gruber, Marvin H J
2013-01-01
Matrix methods have evolved from a tool for expressing statistical problems to an indispensable part of the development, understanding, and use of various types of complex statistical analyses. This evolution has made matrix methods a vital part of statistical education. Traditionally, matrix methods are taught in courses on everything from regression analysis to stochastic processes, thus creating a fractured view of the topic. Matrix Algebra for Linear Models offers readers a unique, unified view of matrix analysis theory (where and when necessary), methods, and their applications. Written f
DEFF Research Database (Denmark)
Gaublomme, Djoere; Buyens, Tom; De Groef, Lies
2014-01-01
regenerative therapies, an improved understanding of axonal outgrowth and the various molecules influencing it, is highly needed. Matrix metalloproteinases (MMPs) constitute a family of zinc-dependent proteases that were sporadically reported to influence axon outgrowth. Using an ex vivo retinal explant model...
Tensor Models: extending the matrix models structures and methods
Dartois, Stephane
2016-01-01
In this text we review a few structural properties of matrix models that should at least partly generalize to random tensor models. We review some aspects of the loop equations for matrix models and their algebraic counterpart for tensor models. Despite the generic title of this review, we, in particular, invoke the Topological Recursion. We explain its appearance in matrix models. Then we state that a family of tensor models provides a natural example which satisfies a version of the most general form of the topological recursion, named the blobbed topological recursion. We discuss the difficulties of extending the technical solutions existing for matrix models to tensor models. Some proofs are not published yet but will be given in a coming paper, the rest of the results are well known in the literature.
... stay angry, or avoid fights altogether? Your children model themselves on you. Departures and Returns Do you or your spouse frequently travel on business? These can be disruptive times for your child and for the family ...
... hypercholesterolemia or early heart attacks High level of LDL cholesterol in either or both parents People from families ... called fibroblasts to see how the body absorbs LDL cholesterol Genetic test for the defect associated with this ...
DEFF Research Database (Denmark)
Kieffer-Kristensen, Rikke; Siersma, Volkert Dirk; Teasdale, Thomas William
2013-01-01
OBJECTIVES: To relate illness and family factors to emotional and behavioural problems in school-age children (7–14 years old) of parents with acquired brain injury and their healthy spouses. PARTICIPANTS, MATERIALS/METHODS: Members of 35 families in which a parent had been diagnosed with acquired...... brain injury participated. Family and brain injury characteristics were reported by the ill and healthy parents. Children self-reported post-traumatic stress symptoms (PSS) using the Child Impact of Events revised (CRIES). Emotional and behavioural problems among the children were also identified...... by the parents using the Achenbach’s Child Behaviour Checklist (CBCL). RESULTS: The family stress variables relating to the healthy spouse in all six comparisons were significant (p
Matrix Product States for Lattice Field Theories
Bañuls, Mari Carmen; Cirac, J Ignacio; Jansen, Karl; Saito, Hana
2013-01-01
The term Tensor Network States (TNS) refers to a number of families of states that represent different ans\\"atze for the efficient description of the state of a quantum many-body system. Matrix Product States (MPS) are one particular case of TNS, and have become the most precise tool for the numerical study of one dimensional quantum many-body systems, as the basis of the Density Matrix Renormalization Group method. Lattice Gauge Theories (LGT), in their Hamiltonian version, offer a challenging scenario for these techniques. While the dimensions and sizes of the systems amenable to TNS studies are still far from those achievable by 4-dimensional LGT tools, Tensor Networks can be readily used for problems which more standard techniques, such as Markov chain Monte Carlo simulations, cannot easily tackle. Examples of such problems are the presence of a chemical potential or out-of-equilibrium dynamics. We have explored the performance of Matrix Product States in the case of the Schwinger model, as a widely used ...
Matrix product states for lattice field theories
Energy Technology Data Exchange (ETDEWEB)
Banuls, M.C.; Cirac, J.I. [Max-Planck-Institut fuer Quantenoptik (MPQ), Garching (Germany); Cichy, K. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Poznan Univ. (Poland). Faculty of Physics; Jansen, K. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Saito, H. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Tsukuba Univ., Ibaraki (Japan). Graduate School of Pure and Applied Sciences
2013-10-15
The term Tensor Network States (TNS) refers to a number of families of states that represent different ansaetze for the efficient description of the state of a quantum many-body system. Matrix Product States (MPS) are one particular case of TNS, and have become the most precise tool for the numerical study of one dimensional quantum many-body systems, as the basis of the Density Matrix Renormalization Group method. Lattice Gauge Theories (LGT), in their Hamiltonian version, offer a challenging scenario for these techniques. While the dimensions and sizes of the systems amenable to TNS studies are still far from those achievable by 4-dimensional LGT tools, Tensor Networks can be readily used for problems which more standard techniques, such as Markov chain Monte Carlo simulations, cannot easily tackle. Examples of such problems are the presence of a chemical potential or out-of-equilibrium dynamics. We have explored the performance of Matrix Product States in the case of the Schwinger model, as a widely used testbench for lattice techniques. Using finite-size, open boundary MPS, we are able to determine the low energy states of the model in a fully non-perturbativemanner. The precision achieved by the method allows for accurate finite size and continuum limit extrapolations of the ground state energy, but also of the chiral condensate and the mass gaps, thus showing the feasibility of these techniques for gauge theory problems.
Development of an integrated system for activity-based profiling of matrix metallo-proteases
Freije, Jan Robert
2006-01-01
Matrix metallo-proteases constitute a family of extracellular zinc-dependent endopeptidases that are involved in degradation of extracellular matrix (ECM) components and other bioactive non-ECM molecules. A plethora of studies have implicated important roles for MMPs in many diseases (including
Development of an integrated system for activity-based profiling of matrix metallo-proteases
Freije, Jan Robert
2006-01-01
Matrix metallo-proteases constitute a family of extracellular zinc-dependent endopeptidases that are involved in degradation of extracellular matrix (ECM) components and other bioactive non-ECM molecules. A plethora of studies have implicated important roles for MMPs in many diseases (including canc
Hingh, I.H.J.T. de; Siemonsma, M.A.; Man, B.M. de; Lomme, R.M.L.M.; Hendriks, T.
2002-01-01
BACKGROUND AND AIMS: The strength of intestinal anastomoses is relatively low in the first days after operation, possibly as a result of localized degradation of the supporting matrix by enzymes from the matrix metalloproteinase (MMP) family. This study examined whether BB-94, a broad spectrum
Matrix Quantization of Turbulence
Floratos, Emmanuel
2011-01-01
Based on our recent work on Quantum Nambu Mechanics $\\cite{af2}$, we provide an explicit quantization of the Lorenz chaotic attractor through the introduction of Non-commutative phase space coordinates as Hermitian $ N \\times N $ matrices in $ R^{3}$. For the volume preserving part, they satisfy the commutation relations induced by one of the two Nambu Hamiltonians, the second one generating a unique time evolution. Dissipation is incorporated quantum mechanically in a self-consistent way having the correct classical limit without the introduction of external degrees of freedom. Due to its volume phase space contraction it violates the quantum commutation relations. We demonstrate that the Heisenberg-Nambu evolution equations for the Matrix Lorenz system develop fast decoherence to N independent Lorenz attractors. On the other hand there is a weak dissipation regime, where the quantum mechanical properties of the volume preserving non-dissipative sector survive for long times.
Velasco, Pedro Pablo Perez
2008-01-01
This book objective is to develop an algebraization of graph grammars. Equivalently, we study graph dynamics. From the point of view of a computer scientist, graph grammars are a natural generalization of Chomsky grammars for which a purely algebraic approach does not exist up to now. A Chomsky (or string) grammar is, roughly speaking, a precise description of a formal language (which in essence is a set of strings). On a more discrete mathematical style, it can be said that graph grammars -- Matrix Graph Grammars in particular -- study dynamics of graphs. Ideally, this algebraization would enforce our understanding of grammars in general, providing new analysis techniques and generalizations of concepts, problems and results known so far.
Dimiev, Stancho; Stoev, Peter; Stoilova, Stanislava
2013-12-01
The notion of anticirculant is ordinary of interest for specialists of general algebra (to see for instance [1]). In this paper we develop some aspects of anticirculants in real function theory. Denoting by X≔x0+jx1+⋯+jmxm, xk∈R, m+1 = 2n, and jk is the k-th degree of the matrix j = (0100...00010...00001...0..................-1000...0), we study the functional anticirculants f(X)≔f0(x0,x1,...,xm)+jf1(x0,x1,...,xm)+⋯+jm-1fm-1(x0,x1,...,xm)+jmfm(x0,x1,...,xm), where fk(x0,x1,...,xm) are smooth functions of 2n real variables. A continuation for complex function theory will appear.
Energy Technology Data Exchange (ETDEWEB)
Hastings, Matthew B [Los Alamos National Laboratory
2009-01-01
We show how to combine the light-cone and matrix product algorithms to simulate quantum systems far from equilibrium for long times. For the case of the XXZ spin chain at {Delta} = 0.5, we simulate to a time of {approx} 22.5. While part of the long simulation time is due to the use of the light-cone method, we also describe a modification of the infinite time-evolving bond decimation algorithm with improved numerical stability, and we describe how to incorporate symmetry into this algorithm. While statistical sampling error means that we are not yet able to make a definite statement, the behavior of the simulation at long times indicates the appearance of either 'revivals' in the order parameter as predicted by Hastings and Levitov (e-print arXiv:0806.4283) or of a distinct shoulder in the decay of the order parameter.
Matrix membranes and integrability
Energy Technology Data Exchange (ETDEWEB)
Zachos, C. [Argonne National Lab., IL (United States); Fairlie, D. [University of Durham (United Kingdom). Dept. of Mathematical Sciences; Curtright, T. [University of Miami, Coral Gables, FL (United States). Dept. of Physics
1997-06-01
This is a pedagogical digest of results reported in Curtright, Fairlie, {ampersand} Zachos 1997, and an explicit implementation of Euler`s construction for the solution of the Poisson Bracket dual Nahm equation. But it does not cover 9 and 10-dimensional systems, and subsequent progress on them Fairlie 1997. Cubic interactions are considered in 3 and 7 space dimensions, respectively, for bosonic membranes in Poisson Bracket form. Their symmetries and vacuum configurations are explored. Their associated first order equations are transformed to Nahm`s equations, and are hence seen to be integrable, for the 3-dimensional case, by virtue of the explicit Lax pair provided. Most constructions introduced also apply to matrix commutator or Moyal Bracket analogs.
... Contents Facts For Families Guide - View by Topic Chinese Facts for Families Guide Facts For Families Guide - Search Spanish Facts for Families Guide Facts for Families - Vietnamese Military Families No. 88; updated March 2017 Global conflict ...
Institute of Scientific and Technical Information of China (English)
潘彬彬; 陈文胜; 徐晨
2009-01-01
非负矩阵分解(NMF)算法可以提取图像的局部特征,然而NMF算法有两个主要缺点:a)当矩阵维数较大时,NMF算法非常耗时;b)当增加新的训练样本或类别时,NMF算法必须进行重复学习.为克服NMF算法这些缺点,提出了一种新的分块NMF算法(BNMF).特别地,该方法还可用于增量学习.通过在FERET和CMU PIE人脸数据库上进行实验,结果表明该算法均优于NMF和PCA算法.
Spherical membranes in Matrix theory
Kabat, D; Kabat, Daniel; Taylor, Washington
1998-01-01
We consider membranes of spherical topology in uncompactified Matrix theory. In general for large membranes Matrix theory reproduces the classical membrane dynamics up to 1/N corrections; for certain simple membrane configurations, the equations of motion agree exactly at finite N. We derive a general formula for the one-loop Matrix potential between two finite-sized objects at large separations. Applied to a graviton interacting with a round spherical membrane, we show that the Matrix potential agrees with the naive supergravity potential for large N, but differs at subleading orders in N. The result is quite general: we prove a pair of theorems showing that for large N, after removing the effects of gravitational radiation, the one-loop potential between classical Matrix configurations agrees with the long-distance potential expected from supergravity. As a spherical membrane shrinks, it eventually becomes a black hole. This provides a natural framework to study Schwarzschild black holes in Matrix theory.
Nonnegative Decomposition of Multivariate Information
Williams, Paul L
2010-01-01
Of the various attempts to generalize information theory to multiple variables, the most widely utilized, interaction information, suffers from the problem that it is sometimes negative. Here we reconsider from first principles the general structure of the information that a set of sources provides about a given variable. We begin with a new definition of redundancy as the minimum information that any source provides about each possible outcome of the variable, averaged over all possible outcomes. We then show how this measure of redundancy induces a lattice over sets of sources that clarifies the general structure of multivariate information. Finally, we use this redundancy lattice to propose a definition of partial information atoms that exhaustively decompose the Shannon information in a multivariate system in terms of the redundancy between synergies of subsets of the sources. Unlike interaction information, the atoms of our partial information decomposition are never negative and always support a clear i...
Linearized supergravity from Matrix theory
Kabat, D; Kabat, Daniel; Taylor, Washington
1998-01-01
We show that the linearized supergravity potential between two objects arising from the exchange of quanta with zero longitudinal momentum is reproduced to all orders in 1/r by terms in the one-loop Matrix theory potential. The essential ingredient in the proof is the identification of the Matrix theory quantities corresponding to moments of the stress tensor and membrane current. We also point out that finite-N Matrix theory violates the Equivalence Principle.
Lectures on Matrix Field Theory
Ydri, Badis
The subject of matrix field theory involves matrix models, noncommutative geometry, fuzzy physics and noncommutative field theory and their interplay. In these lectures, a lot of emphasis is placed on the matrix formulation of noncommutative and fuzzy spaces, and on the non-perturbative treatment of the corresponding field theories. In particular, the phase structure of noncommutative $\\phi^4$ theory is treated in great detail, and an introduction to noncommutative gauge theory is given.
Matrix elements of unstable states
Bernard, V; Meißner, U -G; Rusetsky, A
2012-01-01
Using the language of non-relativistic effective Lagrangians, we formulate a systematic framework for the calculation of resonance matrix elements in lattice QCD. The generalization of the L\\"uscher-Lellouch formula for these matrix elements is derived. We further discuss in detail the procedure of the analytic continuation of the resonance matrix elements into the complex energy plane and investigate the infinite-volume limit.
MacKaay, M A
1996-01-01
In order to construct a representation of the tangle category one needs an enhanced R-matrix. In this paper we define a sufficient and necessary condition for enhancement that can be checked easily for any R-matrix. If the R-matrix can be enhanced, we also show how to construct the additional data that define the enhancement. As a direct consequence we find a sufficient condition for the construction of a knot invariant.
Matrix Models and Gravitational Corrections
Dijkgraaf, R; Temurhan, M; Dijkgraaf, Robbert; Sinkovics, Annamaria; Temurhan, Mine
2002-01-01
We provide evidence of the relation between supersymmetric gauge theories and matrix models beyond the planar limit. We compute gravitational R^2 couplings in gauge theories perturbatively, by summing genus one matrix model diagrams. These diagrams give the leading 1/N^2 corrections in the large N limit of the matrix model and can be related to twist field correlators in a collective conformal field theory. In the case of softly broken SU(N) N=2 super Yang-Mills theories, we find that these exact solutions of the matrix models agree with results obtained by topological field theory methods.
Energy Technology Data Exchange (ETDEWEB)
Dorey, Nick [Department of Applied Mathematics and Theoretical Physics, University of Cambridge,Wilberforce Road, Cambridge, CB3 OWA (United Kingdom); Tong, David [Department of Applied Mathematics and Theoretical Physics, University of Cambridge,Wilberforce Road, Cambridge, CB3 OWA (United Kingdom); Department of Theoretical Physics, TIFR,Homi Bhabha Road, Mumbai 400 005 (India); Stanford Institute for Theoretical Physics,Via Pueblo, Stanford, CA 94305 (United States); Turner, Carl [Department of Applied Mathematics and Theoretical Physics, University of Cambridge,Wilberforce Road, Cambridge, CB3 OWA (United Kingdom)
2016-08-01
We study a U(N) gauged matrix quantum mechanics which, in the large N limit, is closely related to the chiral WZW conformal field theory. This manifests itself in two ways. First, we construct the left-moving Kac-Moody algebra from matrix degrees of freedom. Secondly, we compute the partition function of the matrix model in terms of Schur and Kostka polynomials and show that, in the large N limit, it coincides with the partition function of the WZW model. This same matrix model was recently shown to describe non-Abelian quantum Hall states and the relationship to the WZW model can be understood in this framework.
Dorey, Nick; Turner, Carl
2016-01-01
We study a U(N) gauged matrix quantum mechanics which, in the large N limit, is closely related to the chiral WZW conformal field theory. This manifests itself in two ways. First, we construct the left-moving Kac-Moody algebra from matrix degrees of freedom. Secondly, we compute the partition function of the matrix model in terms of Schur and Kostka polynomials and show that, in the large $N$ limit, it coincides with the partition function of the WZW model. This same matrix model was recently shown to describe non-Abelian quantum Hall states and the relationship to the WZW model can be understood in this framework.
Extended Matrix Variate Hypergeometric Functions and Matrix Variate Distributions
Directory of Open Access Journals (Sweden)
Daya K. Nagar
2015-01-01
Full Text Available Hypergeometric functions of matrix arguments occur frequently in multivariate statistical analysis. In this paper, we define and study extended forms of Gauss and confluent hypergeometric functions of matrix arguments and show that they occur naturally in statistical distribution theory.
Chan, Garnet Kin-Lic; Nakatani, Naoki; Li, Zhendong; White, Steven R
2016-01-01
Current descriptions of the ab initio DMRG algorithm use two superficially different languages: an older language of the renormalization group and renormalized operators, and a more recent language of matrix product states and matrix product operators. The same algorithm can appear dramatically different when written in the two different vocabularies. In this work, we carefully describe the translation between the two languages in several contexts. First, we describe how to efficiently implement the ab-initio DMRG sweep using a matrix product operator based code, and the equivalence to the original renormalized operator implementation. Next we describe how to implement the general matrix product operator/matrix product state algebra within a pure renormalized operator-based DMRG code. Finally, we discuss two improvements of the ab initio DMRG sweep algorithm motivated by matrix product operator language: Hamiltonian compression, and a sum over operators representation that allows for perfect computational par...
Directory of Open Access Journals (Sweden)
Livija Knaflič
1999-12-01
Full Text Available Research in child and adult literacy demonstrates that the achievement and the level of literacy that children attain at school is connected with the social and cultural characteristics and the level of literacy of the child's family. This intergenerational transfer of the level of literacy has motivated the search for different ways of improving the level of literacy.The concept of family literacy is based on the assumption that a higher level of parent literacy means that the children may achieve the same, and it also offers better schooling prospects. Family literacy programmes help families to develop different activities, including reading and writing skills, both in their community and in everyday life.
Family Structure and Family Processes in Mexican American Families
Zeiders, Katharine H.; Roosa, Mark W.; Tein, Jenn-Yun
2011-01-01
Despite increases in single-parent families among Mexican Americans (MA), few studies have examined the association of family structure and family adjustment. Utilizing a diverse sample of 738 Mexican American families (21.7% single parent), the current study examined differences across family structure on early adolescent outcomes, family functioning, and parent-child relationship variables. Results revealed that early adolescents in single parent families reported greater school misconduct,...
Ceramic matrix composite article and process of fabricating a ceramic matrix composite article
Cairo, Ronald Robert; DiMascio, Paul Stephen; Parolini, Jason Robert
2016-01-12
A ceramic matrix composite article and a process of fabricating a ceramic matrix composite are disclosed. The ceramic matrix composite article includes a matrix distribution pattern formed by a manifold and ceramic matrix composite plies laid up on the matrix distribution pattern, includes the manifold, or a combination thereof. The manifold includes one or more matrix distribution channels operably connected to a delivery interface, the delivery interface configured for providing matrix material to one or more of the ceramic matrix composite plies. The process includes providing the manifold, forming the matrix distribution pattern by transporting the matrix material through the manifold, and contacting the ceramic matrix composite plies with the matrix material.
Michelson, J
2004-01-01
The Matrix Theory that has been proposed for various pp wave backgrounds is discussed. Particular emphasis is on the existence of novel nontrivial supersymmetric solutions of the Matrix Theory. These correspond to branes of various shapes (ellipsoidal, paraboloidal, and possibly hyperboloidal) that are unexpected from previous studies of branes in pp wave geometries.
Jairam, Dharmananda; Kiewra, Kenneth A.; Kauffman, Douglas F.; Zhao, Ruomeng
2012-01-01
This study investigated how best to study a matrix. Fifty-three participants studied a matrix topically (1 column at a time), categorically (1 row at a time), or in a unified way (all at once). Results revealed that categorical and unified study produced higher: (a) performance on relationship and fact tests, (b) study material satisfaction, and…
Institute of Scientific and Technical Information of China (English)
曾文平
2003-01-01
A family of three-layer implicit difference Schemes of high accuracy with two parameters for solving high order parabolic equationδu/δt=(-1)m+1δ2mu/δx2m(where m is positive integers) are constructed. In the special case α=1/2, β=0, We obtain a two-layer difference scheme. These schemes are proved to be absolutely stable for arbiratily chosen non-negative parameters, And the order of the truncation error is O((△t)2+(△x)6). They are shown by numerical examples to be effective, and practice consistant with theoretical analysis.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Foster care is conducive to giving orphaned children a better life For most children living in orphanages, having a real home is just a pipe dream. Although they may be well looked after, receive a good education and proper nutrition, the love and care that come from being part of a real family just aren't there.
Institute of Scientific and Technical Information of China (English)
刘才来
2002-01-01
There are four people in my family. They are grandma, father,mother and I. Now we all live in Wuhan. They are from different places. My grandma comes from Sichuan. She likes hot(辣4的) meat very much. She doesn't like bread or noodles at all. She likes vegetables a little. My father is from Guang Zhou.
Institute of Scientific and Technical Information of China (English)
李梅
2012-01-01
There are four people in my family--my parents, my brother and I. My name is Li Mei. I＇m fifteen years old. I am of medium height and build. I like English very much. It＇s very interesting. I can play the piano very well. It makes me feel very happy.
Institute of Scientific and Technical Information of China (English)
LIU YUNYUN
2010-01-01
@@ It took 14 years--and just two min-utes-for an adopted Chinese girl to find her biological family. July 21 this year marked the first anniversary of Haley Butler's finding of her biological parents in Maanshan in east China's Anhui Province.
Wendt, Lisiane Dilli; Ale-Rocha, Rosaly
2016-06-14
Richardiidae are a family of "acalyptrate" Diptera represented by ca. 180 species distributed in the New World, mostly in the Neotropical region. The species that occur in Colombia have received little attention from taxonomists, and the great majority of them are known only from their type localities. Currently, 14 genera and 23 species are known to occur in the country.
DEFF Research Database (Denmark)
Ernst, Erik
2006-01-01
Type abstraction in object-oriented languages embody two techniques, each with its own strenghts and weaknesses. The first technique is extension, yielding abstraction mechanisms with good support for gradual specification. The prime example is inheritance. The second technique is functional abst...... the result as family genericity. The presented language design has been implemented....
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
Discovering her birth parents was an exciting adventure for a 15-year-old girl It took 14 years-and just two minutes-for an adopted Chinese girl to find her biological family.July 21 this year marked the first
Familial hypercholesterolaemia
DEFF Research Database (Denmark)
Versmissen, Jorie; Vongpromek, Ranitha; Yahya, Reyhana
2016-01-01
cholesterol efflux capacity between male familial hypercholesterolaemia (FH) patients with and without CHD relative to their non-FH brothers, and examined HDL constituents including sphingosine-1-phosphate (S1P) and its carrier apolipoprotein M (apoM). RESULTS: Seven FH patients were asymptomatic and six had...
DEFF Research Database (Denmark)
Ernst, Erik
2006-01-01
Type abstraction in object-oriented languages embody two techniques, each with its own strenghts and weaknesses. The first technique is extension, yielding abstraction mechanisms with good support for gradual specification. The prime example is inheritance. The second technique is functional abst...... the result as family genericity. The presented language design has been implemented....
Machining of Metal Matrix Composites
2012-01-01
Machining of Metal Matrix Composites provides the fundamentals and recent advances in the study of machining of metal matrix composites (MMCs). Each chapter is written by an international expert in this important field of research. Machining of Metal Matrix Composites gives the reader information on machining of MMCs with a special emphasis on aluminium matrix composites. Chapter 1 provides the mechanics and modelling of chip formation for traditional machining processes. Chapter 2 is dedicated to surface integrity when machining MMCs. Chapter 3 describes the machinability aspects of MMCs. Chapter 4 contains information on traditional machining processes and Chapter 5 is dedicated to the grinding of MMCs. Chapter 6 describes the dry cutting of MMCs with SiC particulate reinforcement. Finally, Chapter 7 is dedicated to computational methods and optimization in the machining of MMCs. Machining of Metal Matrix Composites can serve as a useful reference for academics, manufacturing and materials researchers, manu...
Matrix Model Approach to Cosmology
Chaney, A; Stern, A
2015-01-01
We perform a systematic search for rotationally invariant cosmological solutions to matrix models, or more specifically the bosonic sector of Lorentzian IKKT-type matrix models, in dimensions $d$ less than ten, specifically $d=3$ and $d=5$. After taking a continuum (or commutative) limit they yield $d-1$ dimensional space-time surfaces, with an attached Poisson structure, which can be associated with closed, open or static cosmologies. For $d=3$, we obtain recursion relations from which it is possible to generate rotationally invariant matrix solutions which yield open universes in the continuum limit. Specific examples of matrix solutions have also been found which are associated with closed and static two-dimensional space-times in the continuum limit. The solutions provide for a matrix resolution of cosmological singularities. The commutative limit reveals other desirable features, such as a solution describing a smooth transition from an initial inflation to a noninflationary era. Many of the $d=3$ soluti...
Matrix convolution operators on groups
Chu, Cho-Ho
2008-01-01
In the last decade, convolution operators of matrix functions have received unusual attention due to their diverse applications. This monograph presents some new developments in the spectral theory of these operators. The setting is the Lp spaces of matrix-valued functions on locally compact groups. The focus is on the spectra and eigenspaces of convolution operators on these spaces, defined by matrix-valued measures. Among various spectral results, the L2-spectrum of such an operator is completely determined and as an application, the spectrum of a discrete Laplacian on a homogeneous graph is computed using this result. The contractivity properties of matrix convolution semigroups are studied and applications to harmonic functions on Lie groups and Riemannian symmetric spaces are discussed. An interesting feature is the presence of Jordan algebraic structures in matrix-harmonic functions.
Investigation on AQ11, ID3 and the Principle of Discernibility Matrix
Institute of Scientific and Technical Information of China (English)
王珏; 崔佳; 赵凯
2001-01-01
The principle of discernibility matrix serves as a tool to discuss and analyze two algorithms of traditional inductive machine learning, AQ11 and ID3. The results are: (1) AQ11 and its family can be completely specified by the principle of discernibility matrix; (2) ID3 can be partly, but not naturally, specified by the principle of discernibility matrix; and (3) The principle of discernibility matrix is employed to analyze Cendrowska sample set, and it shows the weaknesses of knowledge representation style of decision tree in theory.
Learning about Familial Hypercholesterolemia
... terms used on this page Learning About Familial Hypercholesterolemia What is familial hypercholesterolemia? What are the symptoms ... Additional Resources About Familial Hypercholesterolemia What is familial hypercholesterolemia? Familial hypercholesterolemia is an inherited condition that causes ...
... Sex and Birth Control Birth Control Natural Family Planning Natural Family Planning Birth ControlPrevention and WellnessSex and Birth Control Share Natural Family Planning Natural Family PlanningWhat is natural family planning?Natural ...
Manoudi, F; Chagh, R; Es-soussi, M; Asri, F; Tazi, I
2013-09-01
Family violence is a serious public health problem, the scale of which is seriously increasing in Morocco. Although it has existed for a long time, we ignore the real characteristics of this plague in our country; our work consisted in an epidemiological approach of family violence in Marrakech during 2006. After elaborating a questionnaire, which allows the study of the demographic and social profile of the families, the study of violence exercised in the family and the evaluation of the depression in the women, we led an inquiry amongst 265 women. Analysis of the results obtained has allowed us to underline the following characteristics: 16.6% of the women in our sample had been physically beaten; the young age is a risk factor; the age range most affected by violence is in women between the ages of 30 and 40 and which represent 39% of the battered women; domestic violence touches all the social, economic and cultural classes: in our study, 63% of the women having undergone violence were housewives, 25% were managers and 3% senior executives; family problems were the most important cause of violence in our study, representing 32.32%. Requests for money was the cause in 11.3% of the cases, and imposed sexual relations were found in 6.8% of the cases; alcoholism is an aggravating factor of family violence; 27.3% of the spouses who assaulted their wives were drunk; 52% of the assaulted women were victims of violence in childhood and 36% had been witness to their father's violence; in 63.6% of the cases of violence, the children were witnesses, and in 25% of the cases the children were victims of violence at the same time as their mothers; 50% of the women victims of violence did not react, while 38.6% left home, and 9.1 filed for divorce. Thirty-two percent of the assaulted woman had been traumatised by the aggression; the association of depression and violence was very high, 343% of the battered women in our study suffered from severe depression. This work
Manufacturing Titanium Metal Matrix Composites by Consolidating Matrix Coated Fibres
Institute of Scientific and Technical Information of China (English)
Hua-Xin PENG
2005-01-01
Titanium metal matrix composites (TiMMCs) reinforced by continuous silicon carbide fibres are being developed for aerospace applications. TiMMCs manufactured by the consolidation of matrix-coated fibre (MCF) method offer optimum properties because of the resulting uniform fibre distribution, minimum fibre damage and fibre volume fraction control. In this paper, the consolidation of Ti-6Al-4V matrix-coated SiC fibres during vacuum hot pressing has been investigated. Experiments were carried out on multi-ply MCFs under vacuum hot pressing (VHP). In contrast to most of existing studies, the fibre arrangement has been carefully controlled either in square or hexagonal arraysthroughout the consolidated sample. This has enabled the dynamic consolidation behaviour of MCFs to be demonstrated by eliminating the fibre re-arrangement during the VHP process. The microstructural evolution of the matrix coating was reported and the deformation mechanisms involved were discussed.
Institute of Scientific and Technical Information of China (English)
陈君怡
2011-01-01
I have a happy fam_ly．Lookthis is my family photo．This is mydad．He often wears a pair Ofglasses（戴着一副眼镜I_This ismy mum．She is very pretty．Sheloves me very much．The little girlin a red blouse is me．I’m smiling（微笑）．I love my family．
New pole placement algorithm - Polynomial matrix approach
Shafai, B.; Keel, L. H.
1990-01-01
A simple and direct pole-placement algorithm is introduced for dynamical systems having a block companion matrix A. The algorithm utilizes well-established properties of matrix polynomials. Pole placement is achieved by appropriately assigning coefficient matrices of the corresponding matrix polynomial. This involves only matrix additions and multiplications without requiring matrix inversion. A numerical example is given for the purpose of illustration.
High temperature polymer matrix composites
Serafini, Tito T. (Editor)
1987-01-01
These are the proceedings of the High Temperature Polymer Matrix Composites Conference held at the NASA Lewis Research Center on March 16 to 18, 1983. The purpose of the conference is to provide scientists and engineers working in the field of high temperature polymer matrix composites an opportunity to review, exchange, and assess the latest developments in this rapidly expanding area of materials technology. Technical papers are presented in the following areas: (1) matrix development; (2) adhesive development; (3) Characterization; (4) environmental effects; and (5) applications.
Matrix Elements for Hylleraas CI
Harris, Frank E.
The limitation to at most a single interelectron distance in individual configurations of a Hylleraas-type multiconfiguration wave function restricts significantly the types of integrals occurring in matrix elements for energy calculations, but even then if the formulation is not handled efficiently the angular parts of these integrals escalate to create expressions of great complexity. This presentation reviews ways in which the angular-momentum calculus can be employed to systematize and simplify the matrix element formulas, particularly those for the kinetic-energy matrix elements.
[Familial hypercholesterolemia].
Turpin, G; Bruckert, E
1999-12-01
Familial hypercholesterolemia is characterized by a high plasma LDL-cholesterol level. The low-density particles are the end-product of the triglyceride-rich particles, i.e. VLDL, synthetized by the liver. These triglyceride-rich particles are subsequently transformed into intermediate density lipoprotein by the lipoprotein lipase and LDL after further triglyceride hydrolysis by the hepatic lipase. The LDL particles are taken up in all cells by the mean of the LDL receptor. A large body of evidence (including experimental, clinical, epidemiological data as well as the results of large trial with lipid lowering drugs) has accumulated to establish that these particles are one of the major causative factor of atherosclerosis and its complications. Two different mechanisms may be at work in the familial hypercholesterolemia: a mutation in the LDL receptor or a single mutation in the apolipoprotein B100. Specific therapeutic intervention should be undertaken to decrease the risk to develop cardiovascular disease, mainly coronary heart disease. The therapeutic intervention includes both a diet low in saturated fatty acids and cholesterol and statins which are now the first line therapy. Fibrates are proposed to those who do not tolerate statins and LDL-apheresis is associated to statin in the rare homozygous familial hypercholesterolemia.
Han, Fang; Liu, Han
2016-01-01
Correlation matrix plays a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson’s sample correlation matrix. Although Pearson’s sample correlation matrix enjoys various good properties under Gaussian models, its not an effective estimator when facing heavy-tail distributions with possible outliers. As a robust alternative, Han and Liu (2013b) advocated the use of a transformed version of the Kendall’s tau sample correlation matrix in estimating high dimensional latent generalized correlation matrix under the transelliptical distribution family (or elliptical copula). The transelliptical family assumes that after unspecified marginal monotone transformations, the data follow an elliptical distribution. In this paper, we study the theoretical properties of the Kendall’s tau sample correlation matrix and its transformed version proposed in Han and Liu (2013b) for estimating the population Kendall’s tau correlation matrix and the latent Pearson’s correlation matrix under both spectral and restricted spectral norms. With regard to the spectral norm, we highlight the role of “effective rank” in quantifying the rate of convergence. With regard to the restricted spectral norm, we for the first time present a “sign subgaussian condition” which is sufficient to guarantee that the rank-based correlation matrix estimator attains the optimal rate of convergence. In both cases, we do not need any moment condition.
Han, Fang; Liu, Han
2017-02-01
Correlation matrix plays a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of-the-art in estimating large correlation matrices focuses on the use of Pearson's sample correlation matrix. Although Pearson's sample correlation matrix enjoys various good properties under Gaussian models, its not an effective estimator when facing heavy-tail distributions with possible outliers. As a robust alternative, Han and Liu (2013b) advocated the use of a transformed version of the Kendall's tau sample correlation matrix in estimating high dimensional latent generalized correlation matrix under the transelliptical distribution family (or elliptical copula). The transelliptical family assumes that after unspecified marginal monotone transformations, the data follow an elliptical distribution. In this paper, we study the theoretical properties of the Kendall's tau sample correlation matrix and its transformed version proposed in Han and Liu (2013b) for estimating the population Kendall's tau correlation matrix and the latent Pearson's correlation matrix under both spectral and restricted spectral norms. With regard to the spectral norm, we highlight the role of "effective rank" in quantifying the rate of convergence. With regard to the restricted spectral norm, we for the first time present a "sign subgaussian condition" which is sufficient to guarantee that the rank-based correlation matrix estimator attains the optimal rate of convergence. In both cases, we do not need any moment condition.
Matrix metalloproteinases and their expression in mammary gland
Institute of Scientific and Technical Information of China (English)
URIAJOSEA; ZENAWERB
1998-01-01
The matrix metalloproteinases (MMPs) are a family of zine-dependent endopeptidases that play a key role in both normal and pathological processes involving tissue remodeling events.The expression of these proteolytic enzymes is highly regulated by a balance between extracellular matrix (ECM) deposition and its degradation,and is controlled by growth factors,cytokines,hormones,as well as interactions with the ECM macromolecules.Furthermore,the activity of the MMPs is regulated by their natural endogenous inhibitors,which are members of the tissue inhibitor of metalloproteinases (TIMP) family.In the normal mammary gland,MMPs are expressed during ductal development,lobulo-alveolar development in pregnancy and involution after lactation.Under pathological conditions,such as tumorigenesis,the dysregulated expression of MMPs play a role in tumor initiation,progression and malignant conversion as well as facilitating invasion and metastasis of malignant cells through degradation of the ECM and basement membranes.
Matrix models with hard walls: geometry and solutions
Energy Technology Data Exchange (ETDEWEB)
Chekhov, L [Steklov Mathematical Institute, Moscow (Russian Federation); Institute for Theoretical and Experimental Physics, Moscow (Russian Federation); Poncelet Laboratoire International Franco-Russe, Moscow (Russian Federation); Department of Mathematics and Statistics, Concordia University, Montreal (Canada)
2006-07-14
We discuss various aspects of most general multisupport solutions to matrix models in the presence of hard walls, i.e., in the case where the eigenvalue support is confined to subdomains of the real axis. The structure of the solution at the leading order is described by semiclassical or generalized Whitham-Krichever hierarchies as in the unrestricted case. Derivatives of tau-functions for these solutions are associated with families of Riemann surfaces (with possible double points) and satisfy the Witten-Dijkgraaf-Verlinde-Verlinde equations. We then develop the diagrammatic technique for finding free energy of this model in all orders of the 't Hooft expansion in the reciprocal matrix size generalizing the Feynman diagrammatic technique for the Hermitian one-matrix model due to Eynard.
Assessment of Synthetic Matrix Metalloproteinase Inhibitors by Fluorogenic Substrate Assay.
Lively, Ty J; Bosco, Dale B; Khamis, Zahraa I; Sang, Qing-Xiang Amy
2016-01-01
Matrix metalloproteinases (MMPs) are a family of metzincin enzymes that act as the principal regulators and remodelers of the extracellular matrix (ECM). While MMPs are involved in many normal biological processes, unregulated MMP activity has been linked to many detrimental diseases, including cancer, neurodegenerative diseases, stroke, and cardiovascular disease. Developed as tools to investigate MMP function and as potential new therapeutics, matrix metalloproteinase inhibitors (MMPIs) have been designed, synthesized, and tested to regulate MMP activity. This chapter focuses on the use of enzyme kinetics to characterize inhibitors of MMPs. MMP activity is measured via fluorescence spectroscopy using a fluorogenic substrate that contains a 7-methoxycoumarin-4-acetic acid N-succinimidyl ester (Mca) fluorophore and a 2,4-dinitrophenyl (Dpa) quencher separated by a scissile bond. MMP inhibitor (MMPI) potency can be determined from the reduction in fluorescent intensity when compared to the absence of the inhibitor. This chapter describes a technique to characterize a variety of MMPs through enzyme inhibition assays.
Chan, Garnet Kin-Lic; Keselman, Anna; Nakatani, Naoki; Li, Zhendong; White, Steven R.
2016-07-01
Current descriptions of the ab initio density matrix renormalization group (DMRG) algorithm use two superficially different languages: an older language of the renormalization group and renormalized operators, and a more recent language of matrix product states and matrix product operators. The same algorithm can appear dramatically different when written in the two different vocabularies. In this work, we carefully describe the translation between the two languages in several contexts. First, we describe how to efficiently implement the ab initio DMRG sweep using a matrix product operator based code, and the equivalence to the original renormalized operator implementation. Next we describe how to implement the general matrix product operator/matrix product state algebra within a pure renormalized operator-based DMRG code. Finally, we discuss two improvements of the ab initio DMRG sweep algorithm motivated by matrix product operator language: Hamiltonian compression, and a sum over operators representation that allows for perfect computational parallelism. The connections and correspondences described here serve to link the future developments with the past and are important in the efficient implementation of continuing advances in ab initio DMRG and related algorithms.
Chan, Garnet Kin-Lic; Keselman, Anna; Nakatani, Naoki; Li, Zhendong; White, Steven R
2016-07-01
Current descriptions of the ab initio density matrix renormalization group (DMRG) algorithm use two superficially different languages: an older language of the renormalization group and renormalized operators, and a more recent language of matrix product states and matrix product operators. The same algorithm can appear dramatically different when written in the two different vocabularies. In this work, we carefully describe the translation between the two languages in several contexts. First, we describe how to efficiently implement the ab initio DMRG sweep using a matrix product operator based code, and the equivalence to the original renormalized operator implementation. Next we describe how to implement the general matrix product operator/matrix product state algebra within a pure renormalized operator-based DMRG code. Finally, we discuss two improvements of the ab initio DMRG sweep algorithm motivated by matrix product operator language: Hamiltonian compression, and a sum over operators representation that allows for perfect computational parallelism. The connections and correspondences described here serve to link the future developments with the past and are important in the efficient implementation of continuing advances in ab initio DMRG and related algorithms.
Plant Cell Wall Matrix Polysaccharide Biosynthesis
Institute of Scientific and Technical Information of China (English)
Ajay Pal S. Sandhu; Gursharn S. Randhawa; Kanwarpal S. Dhugga
2009-01-01
The wall of an expanding plant cell consists primarily of cellulose microfibrils embedded in a matrix of hemi-cellulosic and pectic polysaccharides along with small amounts of structural and enzymatic proteins. Matrix polysacchar-ides are synthesized in the Golgi and exported to the cell wall by exocytosis, where they intercalate among cellulose microfibrUs, which are made at the plasma membrane and directly deposited into the cell wall. Involvement of Golgi glucan synthesis in auxin-induced cell expansion has long been recognized; however, only recently have the genes corresponding to glucan synthases been identified. Biochemical purification was unsuccessful because of the labile nature and very low abundance of these enzymes. Mutational genetics also proved fruitless. Expression of candidate genes identified through gene expression profiling or comparative genomics in heterologous systems followed by functional characterization has been relatively successful. Several genes from the cellulose synthase-like (Cs/) family have been found to be involved in the synthesis of various hemicellulosic glycans. The usefulness of this approach, however, is limited to those enzymes that probably do not form complexes consisting of unrelated proteins. Nonconventional approaches will continue to incre-mentally unravel the mechanisms of Golgi polysaccharide biosynthesis.
Matrix metalloproteinase inhibition in atherosclerosis and stroke.
Roycik, M D; Myers, J S; Newcomer, R G; Sang, Q-X A
2013-09-01
Matrix metalloproteinases (MMPs) are a family of tightly regulated, zinc-dependent proteases that degrade extracellular matrix (ECM), cell surface, and intracellular proteins. Vascular remodeling, whether as a function of normal physiology or as a consequence of a myriad of pathological processes, requires degradation of the ECM. Thus, the expression and activity of many MMPs are up-regulated in numerous conditions affecting the vasculature and often exacerbate vascular dysfunction. A growing body of evidence supports the rationale of using MMP inhibitors for the treatment of cardiovascular diseases, stroke, and chronic vascular dementia. This manuscript will examine promising targets for MMP inhibition in atherosclerosis and stroke, reviewing findings in preclinical animal models and human patient studies. Strategies for MMP inhibition have progressed beyond chelating the catalytic zinc to functional blocking antibodies and peptides that target either the active site or exosites of the enzyme. While the inhibition of MMP activity presents a rational therapeutic avenue, the multiplicity of roles for MMPs and the non-selective nature of MMP inhibitors that cause unintended side-effects hinder full realization of MMP inhibition as therapy for vascular disease. For optimal therapeutic effects to be realized, specific targets for MMP inhibition in these pathologies must first be identified and then attacked by potent and selective agents during the most appropriate timepoint.
Matrix metalloproteinase imbalance in muscle disuse atrophy.
Giannelli, G; De Marzo, A; Marinosci, F; Antonaci, S
2005-01-01
Muscle atrophy commonly occurs as a consequence of prolonged muscle inactivity, as observed after cast immobilization, bed rest or space flights. The molecular mechanisms responsible for muscle atrophy are still unknown, but a role has been proposed for altered permeability of the sarcolemma and of the surrounding connective tissue. Matrix metallo-proteinases (MMPs) are a family of enzymes with proteolytic activity toward a number of extracellular matrix (ECM) components; they are inhibited by tissue inhibitors of MMPs (TIMPs). In a rat tail-suspension experimental model, we show that after fourteen days of non-weight bearing there is increased expression of MMP-2 in the atrophic soleus and gastrocnemius and decreased expression of TIMP-2. In the same experimental model the expression of Collagen I and Collagen IV, two main ECM components present in the muscles, was reduced and unevenly distributed in unloaded animals. The difference was more evident in the soleus than in the gastrocnemius muscle. This suggests that muscle disuse induces a proteolytic imbalance, which could be responsible for the breakdown of basal lamina structures such as Collagen I and Collagen IV, and that this leads to an altered permeability with consequent atrophy. In conclusion, an MMP-2/TIMP-2 imbalance could have a role in the mechanism underlying muscle disuse atrophy; more studies are needed to expand our molecular knowledge on this issue and to explore the possibility of targeting the proteolytic imbalance with MMP inhibitors.
Raney Distributions and Random Matrix Theory
Forrester, Peter J.; Liu, Dang-Zheng
2015-03-01
Recent works have shown that the family of probability distributions with moments given by the Fuss-Catalan numbers permit a simple parameterized form for their density. We extend this result to the Raney distribution which by definition has its moments given by a generalization of the Fuss-Catalan numbers. Such computations begin with an algebraic equation satisfied by the Stieltjes transform, which we show can be derived from the linear differential equation satisfied by the characteristic polynomial of random matrix realizations of the Raney distribution. For the Fuss-Catalan distribution, an equilibrium problem characterizing the density is identified. The Stieltjes transform for the limiting spectral density of the singular values squared of the matrix product formed from inverse standard Gaussian matrices, and standard Gaussian matrices, is shown to satisfy a variant of the algebraic equation relating to the Raney distribution. Supported on , we show that it too permits a simple functional form upon the introduction of an appropriate choice of parameterization. As an application, the leading asymptotic form of the density as the endpoints of the support are approached is computed, and is shown to have some universal features.
National Oceanic and Atmospheric Administration, Department of Commerce — This data set was taken from CRD 08-18 at the NEFSC. Specifically, the Gulf of Maine diet matrix was developed for the EMAX exercise described in that center...
Linear connections on matrix geometries
Madore, J; Mourad, J; Madore, John; Masson, Thierry; Mourad, Jihad
1994-01-01
A general definition of a linear connection in noncommutative geometry has been recently proposed. Two examples are given of linear connections in noncommutative geometries which are based on matrix algebras. They both possess a unique metric connection.
Matrix Quantum Mechanics from Qubits
Hartnoll, Sean A; Mazenc, Edward A
2016-01-01
We introduce a transverse field Ising model with order N^2 spins interacting via a nonlocal quartic interaction. The model has an O(N,Z), hyperoctahedral, symmetry. We show that the large N partition function admits a saddle point in which the symmetry is enhanced to O(N). We further demonstrate that this `matrix saddle' correctly computes large N observables at weak and strong coupling. The matrix saddle undergoes a continuous quantum phase transition at intermediate couplings. At the transition the matrix eigenvalue distribution becomes disconnected. The critical excitations are described by large N matrix quantum mechanics. At the critical point, the low energy excitations are waves propagating in an emergent 1+1 dimensional spacetime.
Energy Technology Data Exchange (ETDEWEB)
Descouvemont, P; Baye, D [Physique Nucleaire Theorique et Physique Mathematique, C.P. 229, Universite Libre de Bruxelles (ULB), B 1050 Brussels (Belgium)], E-mail: pdesc@ulb.ac.be, E-mail: dbaye@ulb.ac.be
2010-03-15
The different facets of the R-matrix method are presented pedagogically in a general framework. Two variants have been developed over the years: (i) The 'calculable' R-matrix method is a calculational tool to derive scattering properties from the Schroedinger equation in a large variety of physical problems. It was developed rather independently in atomic and nuclear physics with too little mutual influence. (ii) The 'phenomenological' R-matrix method is a technique to parametrize various types of cross sections. It was mainly (or uniquely) used in nuclear physics. Both directions are explained by starting from the simple problem of scattering by a potential. They are illustrated by simple examples in nuclear and atomic physics. In addition to elastic scattering, the R-matrix formalism is applied to inelastic and radiative-capture reactions. We also present more recent and more ambitious applications of the theory in nuclear physics.
Matrix analysis of electrical machinery
Hancock, N N
2013-01-01
Matrix Analysis of Electrical Machinery, Second Edition is a 14-chapter edition that covers the systematic analysis of electrical machinery performance. This edition discusses the principles of various mathematical operations and their application to electrical machinery performance calculations. The introductory chapters deal with the matrix representation of algebraic equations and their application to static electrical networks. The following chapters describe the fundamentals of different transformers and rotating machines and present torque analysis in terms of the currents based on the p
New tools for investigating positive maps in matrix algebras
Zwolak, Justyna Pytel
2012-01-01
We provide a novel tool which may be used to construct new examples of positive maps in matrix algebras (or, equivalently, entanglement witnesses). It turns out that this can be used to prove positivity of several well known maps (such as reduction map, generalized reduction, Robertson map, and many others). Furthermore, we use it to construct a new family of linear maps and prove that they are positive, indecomposable and (nd)optimal.
Life distributions structure of nonparametric, semiparametric, and parametric families
Marshall, Albert W
2007-01-01
This book is devoted to the study of univariate distributions appropriate for the analyses of data known to be nonnegative. The book includes much material from reliability theory in engineering and survival analysis in medicine.
SVD row or column symmetric matrix
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A new architecture for row or column symmetric matrix called extended matrix is defined, and a precise correspondence of the singular values and singular vectors between the extended matrix and its original (namely, the mother matrix) is derived. As an illustration of potential, we show that, for a class of extended matrices, the singular value decomposition using the mother matrix rather than the extended matrix per se can save the CPU time and memory without loss of numerical precision.
Minimal Realizations of Supersymmetry for Matrix Hamiltonians
Andrianov, Alexandr A
2014-01-01
The notions of weak and strong minimizability of a matrix intertwining operator are introduced. Criterion of strong minimizability of a matrix intertwining operator is revealed. Criterion and sufficient condition of existence of a constant symmetry matrix for a matrix Hamiltonian are presented. A method of constructing of a matrix Hamiltonian with a given constant symmetry matrix in terms of a set of arbitrary scalar functions and eigen- and associated vectors of this matrix is offered. Examples of constructing of $2\\times2$ matrix Hamiltonians with given symmetry matrices for the cases of different structure of Jordan form of these matrices are elucidated.
Sparse Planar Array Synthesis Using Matrix Enhancement and Matrix Pencil
Directory of Open Access Journals (Sweden)
Mei-yan Zheng
2013-01-01
Full Text Available The matrix enhancement and matrix pencil (MEMP plays important roles in modern signal processing applications. In this paper, MEMP is applied to attack the problem of two-dimensional sparse array synthesis. Firstly, the desired array radiation pattern, as the original pattern for approximating, is sampled to form an enhanced matrix. After performing the singular value decomposition (SVD and discarding the insignificant singular values according to the prior approximate error, the minimum number of elements can be obtained. Secondly, in order to obtain the eigenvalues, the generalized eigen-decomposition is employed on the approximate matrix, which is the optimal low-rank approximation of the enhanced matrix corresponding to sparse planar array, and then the ESPRIT algorithm is utilized to pair the eigenvalues related to each dimension of the planar array. Finally, element positions and excitations of the sparse planar array are calculated according to the correct pairing of eigenvalues. Simulation results are presented to illustrate the effectiveness of the proposed approach.
Familial aggregation and childhood blood pressure.
Wang, Xiaoling; Xu, Xiaojing; Su, Shaoyong; Snieder, Harold
2015-01-01
There is growing concern about elevated blood pressure (BP) in children. The evidence for familial aggregation of childhood BP is substantial. Twin studies have shown that a large part of the familial aggregation of childhood BP is due to genes. The first part of this review provides the latest progress in gene finding for childhood BP, focusing on the combined effects of multiple loci identified from the genome-wide association studies on adult BP. We further review the evidence on the contribution of the genetic components of other family risk factors to the familial aggregation of childhood BP including obesity, birth weight, sleep quality, sodium intake, parental smoking, and socioeconomic status. At the end, we emphasize the promise of using genomic-relatedness-matrix restricted maximum likelihood (GREML) analysis, a method that uses genome-wide data from unrelated individuals, in answering a number of unsolved questions in the familial aggregation of childhood BP.
de Abreu, J.R.F.; de Launay, D.; Sanders, M.E.; Grabiec, A.M.; van de Sande, M.G.; Tak, P.P.; Reedquist, K.A.
2009-01-01
Introduction Fibroblast-like synoviocytes (FLS) from rheumatoid arthritis ( RA) patients share many similarities with transformed cancer cells, including spontaneous production of matrix metalloproteinases ( MMPs). Altered or chronic activation of proto-oncogenic Ras family GTPases is thought to
Freije, Robert; Klein, Theo; Ooms, Bert; Kauffman, Henk F.; Bischoff, Rainer
2008-01-01
Matrix metalloproteases (MMPs) comprise a family of enzymes that play important roles in mediating angiogenesis, the remodelling of tissues and in cancer metastasis. Consequently, they are attractive targets for therapeutic intervention in chronic inflammation, cancer and neurological disorders. In
Metal-Matrix/Hollow-Ceramic-Sphere Composites
Baker, Dean M.
2011-01-01
A family of metal/ceramic composite materials has been developed that are relatively inexpensive, lightweight alternatives to structural materials that are typified by beryllium, aluminum, and graphite/epoxy composites. These metal/ceramic composites were originally intended to replace beryllium (which is toxic and expensive) as a structural material for lightweight mirrors for aerospace applications. These materials also have potential utility in automotive and many other terrestrial applications in which there are requirements for lightweight materials that have high strengths and other tailorable properties as described below. The ceramic component of a material in this family consists of hollow ceramic spheres that have been formulated to be lightweight (0.5 g/cm3) and have high crush strength [40.80 ksi (.276.552 MPa)]. The hollow spheres are coated with a metal to enhance a specific performance . such as shielding against radiation (cosmic rays or x rays) or against electromagnetic interference at radio and lower frequencies, or a material to reduce the coefficient of thermal expansion (CTE) of the final composite material, and/or materials to mitigate any mismatch between the spheres and the matrix metal. Because of the high crush strength of the spheres, the initial composite workpiece can be forged or extruded into a high-strength part. The total time taken in processing from the raw ingredients to a finished part is typically 10 to 14 days depending on machining required.
Enamel matrix proteins; old molecules for new applications.
Lyngstadaas, S P; Wohlfahrt, J C; Brookes, S J; Paine, M L; Snead, M L; Reseland, J E
2009-08-01
Emdogain (enamel matrix derivative, EMD) is well recognized in periodontology, where it is used as a local adjunct to periodontal surgery to stimulate regeneration of periodontal tissues lost to periodontal disease. The biological effect of EMD is through stimulation of local growth factor secretion and cytokine expression in the treated tissues, inducing a regenerative process that mimics odontogenesis. The major (>95%) component of EMD is Amelogenins (Amel). No other active components have so far been isolated from EMD, and several studies have shown that purified amelogenins can induce the same effect as the complete EMD. Amelogenins comprise a family of highly conserved extracellular matrix proteins derived from one gene. Amelogenin structure and function is evolutionary well conserved, suggesting a profound role in biomineralization and hard tissue formation. A special feature of amelogenins is that under physiological conditions the proteins self-assembles into nanospheres that constitute an extracellular matrix. In the body, this matrix is slowly digested by specific extracellular proteolytic enzymes (matrix metalloproteinase) in a controlled process, releasing bioactive peptides to the surrounding tissues for weeks after application. Based on clinical and experimental observations in periodontology indicating that amelogenins can have a significant positive influence on wound healing, bone formation and root resorption, several new applications for amelogenins have been suggested. New experiments now confirm that amelogenins have potential for being used also in the fields of endodontics, bone regeneration, implantology, traumatology, and wound care.
The Astrobiology Matrix and the "Drake Matrix" in Education
Mizser, A.; Kereszturi, A.
2003-01-01
We organized astrobiology lectures in the Eotvos Lorand University of Sciences and the Polaris Observatory in 2002. We present here the "Drake matrix" for the comparison of the astrobiological potential of different bodies [1], and astrobiology matrix for the visualization of the interdisciplinary connections between different fields of astrobiology. Conclusion: In Hungary it is difficult to integrate astrobiology in the education system but the great advantage is that it can connect different scientific fields and improve the view of students. We would like to get in contact with persons and organizations who already have experience in the education of astrobiology.
A survey of matrix theory and matrix inequalities
Marcus, Marvin
2010-01-01
Written for advanced undergraduate students, this highly regarded book presents an enormous amount of information in a concise and accessible format. Beginning with the assumption that the reader has never seen a matrix before, the authors go on to provide a survey of a substantial part of the field, including many areas of modern research interest.Part One of the book covers not only the standard ideas of matrix theory, but ones, as the authors state, ""that reflect our own prejudices,"" among them Kronecker products, compound and induced matrices, quadratic relations, permanents, incidence
... Life Listen Español Text Size Email Print Share Roles Within the Family Page Content Article Body Families ... family unit, and which rights, privileges, obligations, and roles are assigned to each family member. In most ...
Matrix factorizations and elliptic fibrations
Omer, Harun
2016-09-01
I use matrix factorizations to describe branes at simple singularities of elliptic fibrations. Each node of the corresponding Dynkin diagrams of the ADE-type singularities is associated with one indecomposable matrix factorization which can be deformed into one or more factorizations of lower rank. Branes with internal fluxes arise naturally as bound states of the indecomposable factorizations. Describing branes in such a way avoids the need to resolve singularities. This paper looks at gauge group breaking from E8 fibers down to SU (5) fibers due to the relevance of such fibrations for local F-theory GUT models. A purpose of this paper is to understand how the deformations of the singularity are understood in terms of its matrix factorizations. By systematically factorizing the elliptic fiber equation, this paper discusses geometries which are relevant for building semi-realistic local models. In the process it becomes evident that breaking patterns which are identical at the level of the Kodaira type of the fibers can be inequivalent at the level of matrix factorizations. Therefore the matrix factorization picture supplements information which the conventional less detailed descriptions lack.
Octonionic matrix representation and electromagnetism
Energy Technology Data Exchange (ETDEWEB)
Chanyal, B. C. [Kumaun University, S. S. J. Campus, Almora (India)
2014-12-15
Keeping in mind the important role of octonion algebra, we have obtained the electromagnetic field equations of dyons with an octonionic 8 x 8 matrix representation. In this paper, we consider the eight - dimensional octonionic space as a combination of two (external and internal) four-dimensional spaces for the existence of magnetic monopoles (dyons) in a higher-dimensional formalism. As such, we describe the octonion wave equations in terms of eight components from the 8 x 8 matrix representation. The octonion forms of the generalized potential, fields and current source of dyons in terms of 8 x 8 matrix are discussed in a consistent manner. Thus, we have obtained the generalized Dirac-Maxwell equations of dyons from an 8x8 matrix representation of the octonion wave equations in a compact and consistent manner. The generalized Dirac-Maxwell equations are fully symmetric Maxwell equations and allow for the possibility of magnetic charges and currents, analogous to electric charges and currents. Accordingly, we have obtained the octonionic Dirac wave equations in an external field from the matrix representation of the octonion-valued potentials of dyons.
Matrix factorizations and elliptic fibrations
Directory of Open Access Journals (Sweden)
Harun Omer
2016-09-01
Full Text Available I use matrix factorizations to describe branes at simple singularities of elliptic fibrations. Each node of the corresponding Dynkin diagrams of the ADE-type singularities is associated with one indecomposable matrix factorization which can be deformed into one or more factorizations of lower rank. Branes with internal fluxes arise naturally as bound states of the indecomposable factorizations. Describing branes in such a way avoids the need to resolve singularities. This paper looks at gauge group breaking from E8 fibers down to SU(5 fibers due to the relevance of such fibrations for local F-theory GUT models. A purpose of this paper is to understand how the deformations of the singularity are understood in terms of its matrix factorizations. By systematically factorizing the elliptic fiber equation, this paper discusses geometries which are relevant for building semi-realistic local models. In the process it becomes evident that breaking patterns which are identical at the level of the Kodaira type of the fibers can be inequivalent at the level of matrix factorizations. Therefore the matrix factorization picture supplements information which the conventional less detailed descriptions lack.
Three-body matrix elements for calculations of mean field and exp(S) ground state correlations
Mihaila, B; Mihaila, Bogdan; Heisenberg, Jochen H.
1999-01-01
In this document we present our approach to the computation of three-body matrix elements, based on the Urbana family of three-nucleon potentials. The calculations refer only to the necessary matrix elements needed to include the three-nucleon interaction in the manner presented in nucl-th/9912023.
Directory of Open Access Journals (Sweden)
Ruifeng Wu
2014-01-01
shifts of the function f(x(x∈ℝ to approximate the derivatives of f(x, we propose a family of modified even order Bernoulli-type multiquadric quasi-interpolants which do not require the derivatives of the function approximated at each node and can satisfy any degree polynomial reproduction property. Error estimate indicates that our operators could provide the desired precision by choosing a suitable shape-preserving parameter c and a nonnegative integer m. Numerical comparisons show that this technique provides a higher degree of accuracy. Finally, applying our operators to the fitting of discrete solutions of initial value problems, we find that our method has smaller errors than the Runge-Kutta method of order 4 and Wang et al.’s quasi-interpolation scheme.
Integrating Family Resilience and Family Stress Theory.
Patterson, Joan M.
2002-01-01
The construct, family resilience, is defined differently by practitioners and researchers. This study tries to clarify the concept of family resilience. The foundation is family stress and coping theory, particularly the stress models that emphasize adaptation processes in families exposed to major adversities. (JDM)
Dijkgraaf, R; Dijkgraaf, Robbert; Vafa, Cumrun
2002-01-01
We point out two extensions of the relation between matrix models, topological strings and N=1 supersymmetric gauge theories. First, we note that by considering double scaling limits of unitary matrix models one can obtain large N duals of the local Calabi-Yau geometries that engineer N=2 gauge theories. In particular, a double scaling limit of the Gross-Witten one-plaquette lattice model gives the SU(2) Seiberg-Witten solution, including its induced gravitational corrections. Secondly, we point out that the effective superpotential terms for N=1 ADE quiver gauge theories is similarly computed by large multi-matrix models, that have been considered in the context of ADE minimal models on random surfaces. The associated spectral curves are multiple branched covers obtained as Virasoro and W-constraints of the partition function.
Dijkgraaf, Robbert; Vafa, Cumrun
2002-11-01
We point out two extensions of the relation between matrix models, topological strings and N=1 supersymmetric gauge theories. First, we note that by considering double scaling limits of unitary matrix models one can obtain large- N duals of the local Calabi-Yau geometries that engineer N=2 gauge theories. In particular, a double scaling limit of the Gross-Witten one-plaquette lattice model gives the SU(2) Seiberg-Witten solution, including its induced gravitational corrections. Secondly, we point out that the effective superpotential terms for N=1 ADE quiver gauge theories is similarly computed by large- N multi-matrix models, that have been considered in the context of ADE minimal models on random surfaces. The associated spectral curves are multiple branched covers obtained as Virasoro and W-constraints of the partition function.
Energy Technology Data Exchange (ETDEWEB)
Dijkgraaf, Robbert E-mail: rhd@science.uva.nl; Vafa, Cumrun
2002-11-11
We point out two extensions of the relation between matrix models, topological strings and N=1 supersymmetric gauge theories. First, we note that by considering double scaling limits of unitary matrix models one can obtain large-N duals of the local Calabi-Yau geometries that engineer N=2 gauge theories. In particular, a double scaling limit of the Gross-Witten one-plaquette lattice model gives the SU(2) Seiberg-Witten solution, including its induced gravitational corrections. Secondly, we point out that the effective superpotential terms for N=1 ADE quiver gauge theories is similarly computed by large-N multi-matrix models, that have been considered in the context of ADE minimal models on random surfaces. The associated spectral curves are multiple branched covers obtained as Virasoro and W-constraints of the partition function.
Lectures on matrix field theory
Ydri, Badis
2017-01-01
These lecture notes provide a systematic introduction to matrix models of quantum field theories with non-commutative and fuzzy geometries. The book initially focuses on the matrix formulation of non-commutative and fuzzy spaces, followed by a description of the non-perturbative treatment of the corresponding field theories. As an example, the phase structure of non-commutative phi-four theory is treated in great detail, with a separate chapter on the multitrace approach. The last chapter offers a general introduction to non-commutative gauge theories, while two appendices round out the text. Primarily written as a self-study guide for postgraduate students – with the aim of pedagogically introducing them to key analytical and numerical tools, as well as useful physical models in applications – these lecture notes will also benefit experienced researchers by providing a reference guide to the fundamentals of non-commutative field theory with an emphasis on matrix models and fuzzy geometries.
Noncommutative spaces from matrix models
Lu, Lei
Noncommutative (NC) spaces commonly arise as solutions to matrix model equations of motion. They are natural generalizations of the ordinary commutative spacetime. Such spaces may provide insights into physics close to the Planck scale, where quantum gravity becomes relevant. Although there has been much research in the literature, aspects of these NC spaces need further investigation. In this dissertation, we focus on properties of NC spaces in several different contexts. In particular, we study exact NC spaces which result from solutions to matrix model equations of motion. These spaces are associated with finite-dimensional Lie-algebras. More specifically, they are two-dimensional fuzzy spaces that arise from a three-dimensional Yang-Mills type matrix model, four-dimensional tensor-product fuzzy spaces from a tensorial matrix model, and Snyder algebra from a five-dimensional tensorial matrix model. In the first part of this dissertation, we study two-dimensional NC solutions to matrix equations of motion of extended IKKT-type matrix models in three-space-time dimensions. Perturbations around the NC solutions lead to NC field theories living on a two-dimensional space-time. The commutative limit of the solutions are smooth manifolds which can be associated with closed, open and static two-dimensional cosmologies. One particular solution is a Lorentzian fuzzy sphere, which leads to essentially a fuzzy sphere in the Minkowski space-time. In the commutative limit, this solution leads to an induced metric that does not have a fixed signature, and have a non-constant negative scalar curvature, along with singularities at two fixed latitudes. The singularities are absent in the matrix solution which provides a toy model for resolving the singularities of General relativity. We also discussed the two-dimensional fuzzy de Sitter space-time, which has irreducible representations of su(1,1) Lie-algebra in terms of principal, complementary and discrete series. Field