Sample records for maximal discrete subspace

  1. Constraining Torsion in Maximally symmetric (sub)spaces

    Sur, Sourav


    We look into the general aspects of space-time symmetries in presence of torsion, and how the latter is affected by such symmetries. Focusing in particular to space-times which either exhibit maximal symmetry on their own, or could be decomposed to maximally symmetric subspaces, we work out the constraints on torsion in two different theoretical schemes. We show that at least for a completely antisymmetric torsion tensor (for e.g. the one motivated from string theory), an equivalence is set between these two schemes, as the non-vanishing independent torsion tensor components turn out to be the same.

  2. LBAS: Lanczos Bidiagonalization with Subspace Augmentation for Discrete Inverse Problems

    Hansen, Per Christian; Abe, Kyniyoshi

    The regularizing properties of Lanczos bidiagonalization are powerful when the underlying Krylov subspace captures the dominating components of the solution. In some applications the regularized solution can be further improved by augmenting the Krylov subspace with a low-dimensional subspace...

  3. Exact solutions and maximal dimension of invariant subspaces of time fractional coupled nonlinear partial differential equations

    Sahadevan, R.; Prakash, P.


    We show how invariant subspace method can be extended to time fractional coupled nonlinear partial differential equations and construct their exact solutions. Effectiveness of the method has been illustrated through time fractional Hunter-Saxton equation, time fractional coupled nonlinear diffusion system, time fractional coupled Boussinesq equation and time fractional Whitman-Broer-Kaup system. Also we explain how maximal dimension of the time fractional coupled nonlinear partial differential equations can be estimated.

  4. On the Maximal Dimension of a Completely Entangled Subspace for Finite Level Quantum Systems

    K R Parthasarathy


    Let $\\mathcal{H}_i$ be a finite dimensional complex Hilbert space of dimension $d_i$ associated with a finite level quantum system $A_i$ for $i=1, 2,\\ldots,k$. A subspace $S\\subset\\mathcal{H} = \\mathcal{H}_{A_1 A_2\\ldots A_k} = \\mathcal{H}_1 \\otimes \\mathcal{H}_2 \\otimes\\cdots\\otimes \\mathcal{H}_k$ is said to be completely entangled if it has no non-zero product vector of the form $u_1 \\otimes u_2 \\otimes\\cdots\\otimes u_k$ with $u_i$ in $\\mathcal{H}_i$ for each . Using the methods of elementary linear algebra and the intersection theorem for projective varieties in basic algebraic geometry we prove that $$\\max\\limits_{S\\in\\mathcal{E}}\\dim S=d_1 d_2\\ldots d_k-(d_1+\\cdots +d_k)+k-1,$$ where $\\mathcal{E}$ is the collection of all completely entangled subspaces. When $\\mathcal{H}_1 = \\mathcal{H}_2$ and $k = 2$ an explicit orthonormal basis of a maximal completely entangled subspace of $\\mathcal{H}_1 \\otimes \\mathcal{H}_2$ is given. We also introduce a more delicate notion of a perfectly entangled subspace for a multipartite quantum system, construct an example using the theory of stabilizer quantum codes and pose a problem.

  5. Subspace-based identification of discrete time-delay system

    Qiang LIU; Jia-chen MA


    We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant (LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search (ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.



    For a continuous, increasing function ω: R+ → R+\\{0} of finite exponential type, this paper introduces the set Z(A, ω) of all x in a Banach space X for which the second order abstract differential equation (2) has a mild solution such that [ω(t)]-1u(t,x) is uniformly continues on R+, and show that Z(A, ω) is a maximal Banach subspace continuously embedded in X, where A ∈ B(X) is closed. Moreover, A|z(A,ω) generates an O(ω(t))strongly continuous cosine operator function family.

  7. On a discrete version of Tanaka's theorem for maximal functions

    Bober, Jonathan; Hughes, Kevin; Pierce, Lillian B


    In this paper we prove a discrete version of Tanaka's Theorem \\cite{Ta} for the Hardy-Littlewood maximal operator in dimension $n=1$, both in the non-centered and centered cases. For the discrete non-centered maximal operator $\\wM $ we prove that, given a function $f: \\Z \\to \\R$ of bounded variation,

  8. Maximal energy extraction under discrete diffusive exchange

    Hay, M. J., E-mail: [Department of Astrophysical Sciences, Princeton University, Princeton, New Jersey 08544 (United States); Schiff, J. [Department of Mathematics, Bar-Ilan University, Ramat Gan 52900 (Israel); Fisch, N. J. [Department of Astrophysical Sciences, Princeton University, Princeton, New Jersey 08544 (United States); Princeton Plasma Physics Laboratory, Princeton, New Jersey 08543 (United States)


    Waves propagating through a bounded plasma can rearrange the densities of states in the six-dimensional velocity-configuration phase space. Depending on the rearrangement, the wave energy can either increase or decrease, with the difference taken up by the total plasma energy. In the case where the rearrangement is diffusive, only certain plasma states can be reached. It turns out that the set of reachable states through such diffusive rearrangements has been described in very different contexts. Building upon those descriptions, and making use of the fact that the plasma energy is a linear functional of the state densities, the maximal extractable energy under diffusive rearrangement can then be addressed through linear programming.

  9. Maximal energy extraction under discrete diffusive exchange

    Hay, Michael J; Fisch, Nathaniel J


    Waves propagating through a bounded plasma can rearrange the densities of states in the six-dimensional velocity-configuration phase space. Depending on the rearrangement, the wave energy can either increase or decrease, with the difference taken up by the total plasma energy. In the case where the rearrangement is diffusive, only certain plasma states can be reached. It turns out that the set of reachable states through such diffusive rearrangements has been described in very different contexts. Building upon those descriptions, and making use of the fact that the plasma energy is a linear functional of the state densities, the maximal extractable energy under diffusive rearrangement can then be addressed through linear programming.

  10. Maximal Dimension of Invariant Subspaces to Systems of Nonlinear Evolution Equations

    Shoufeng SHEN; ChangZheng QU; Yongyang JIN; Lina JI


    In this paper,the dimension of invariant subspaces admitted by nonlinear systems is estimated under certain conditions.It is shown that if the two-component nonlinear vector differential operator F =(F1,F2) with orders {k1,k2} (k1 ≥ k2) preserves the invariant subspace W1n1 × W2n2 (n1 ≥ n2),then n1 - n2 ≤ k2,n1 ≤ 2(k1 + k2) + 1,where Wqnq is the space generated by solutions of a linear ordinary differential equation of order nq (q =1,2).Several examples including the (1+1)-dimensional diffusion system and It(o)'s type,Drinfel'd-Sokolov-Wilson's type and Whitham-Broer-Kaup's type equations are presented to illustrate the result.Furthermore,the estimate of dimension for m-component nonlinear systems is also given.

  11. Discrete-State Stochastic Models of Calcium-Regulated Calcium Influx and Subspace Dynamics Are Not Well-Approximated by ODEs That Neglect Concentration Fluctuations

    Seth H. Weinberg


    Full Text Available Cardiac myocyte calcium signaling is often modeled using deterministic ordinary differential equations (ODEs and mass-action kinetics. However, spatially restricted “domains” associated with calcium influx are small enough (e.g., 10−17 liters that local signaling may involve 1–100 calcium ions. Is it appropriate to model the dynamics of subspace calcium using deterministic ODEs or, alternatively, do we require stochastic descriptions that account for the fundamentally discrete nature of these local calcium signals? To address this question, we constructed a minimal Markov model of a calcium-regulated calcium channel and associated subspace. We compared the expected value of fluctuating subspace calcium concentration (a result that accounts for the small subspace volume with the corresponding deterministic model (an approximation that assumes large system size. When subspace calcium did not regulate calcium influx, the deterministic and stochastic descriptions agreed. However, when calcium binding altered channel activity in the model, the continuous deterministic description often deviated significantly from the discrete stochastic model, unless the subspace volume is unrealistically large and/or the kinetics of the calcium binding are sufficiently fast. This principle was also demonstrated using a physiologically realistic model of calmodulin regulation of L-type calcium channels introduced by Yue and coworkers.

  12. Long memory analysis by using maximal overlapping discrete wavelet transform

    Shafie, Nur Amalina binti; Ismail, Mohd Tahir; Isa, Zaidi


    Long memory process is the asymptotic decay of the autocorrelation or spectral density around zero. The main objective of this paper is to do a long memory analysis by using the Maximal Overlapping Discrete Wavelet Transform (MODWT) based on wavelet variance. In doing so, stock market of Malaysia, China, Singapore, Japan and United States of America are used. The risk of long term and short term investment are also being looked into. MODWT can be analyzed with time domain and frequency domain simultaneously and decomposing wavelet variance to different scales without loss any information. All countries under studied show that they have long memory. Subprime mortgage crisis in 2007 is occurred in the United States of America are possible affect to the major trading countries. Short term investment is more risky than long term investment.

  13. Extremal sizes of subspace partitions

    Heden, Olof; Nastase, Esmeralda; Sissokho, Papa


    A subspace partition $\\Pi$ of $V=V(n,q)$ is a collection of subspaces of $V$ such that each 1-dimensional subspace of $V$ is in exactly one subspace of $\\Pi$. The size of $\\Pi$ is the number of its subspaces. Let $\\sigma_q(n,t)$ denote the minimum size of a subspace partition of $V$ in which the largest subspace has dimension $t$, and let $\\rho_q(n,t)$ denote the maximum size of a subspace partition of $V$ in which the smallest subspace has dimension $t$. In this paper, we determine the values of $\\sigma_q(n,t)$ and $\\rho_q(n,t)$ for all positive integers $n$ and $t$. Furthermore, we prove that if $n\\geq 2t$, then the minimum size of a maximal partial $t$-spread in $V(n+t-1,q)$ is $\\sigma_q(n,t)$.

  14. Maximal Regularity of the Discrete Harmonic Oscillator Equation

    Airton Castro


    Full Text Available We give a representation of the solution for the best approximation of the harmonic oscillator equation formulated in a general Banach space setting, and a characterization of lp-maximal regularity—or well posedness—solely in terms of R-boundedness properties of the resolvent operator involved in the equation.

  15. Continuous or discrete attractors in neural circuits? A self-organized switch at maximal entropy

    Bernacchia, Alberto


    A recent experiment suggests that neural circuits may alternatively implement continuous or discrete attractors, depending on the training set up. In recurrent neural network models, continuous and discrete attractors are separately modeled by distinct forms of synaptic prescriptions (learning rules). Here, we report a solvable network model, endowed with Hebbian synaptic plasticity, which is able to learn either discrete or continuous attractors, depending on the frequency of presentation of stimuli and on the structure of sensory coding. A continuous attractor is learned when experience matches sensory coding, i.e. when the distribution of experienced stimuli matches the distribution of preferred stimuli of neurons. In that case, there is no processing of sensory information and neural activity displays maximal entropy. If experience goes beyond sensory coding, processing is initiated and the continuous attractor is destabilized into a set of discrete attractors.

  16. A Heuristic Optimal Discrete Bit Allocation Algorithm for Margin Maximization in DMT Systems

    Dong Shi-Wei


    Full Text Available A heuristic optimal discrete bit allocation algorithm is proposed for solving the margin maximization problem in discrete multitone (DMT systems. Starting from an initial equal power assignment bit distribution, the proposed algorithm employs a multistaged bit rate allocation scheme to meet the target rate. If the total bit rate is far from the target rate, a multiple-bits loading procedure is used to obtain a bit allocation close to the target rate. When close to the target rate, a parallel bit-loading procedure is used to achieve the target rate and this is computationally more efficient than conventional greedy bit-loading algorithm. Finally, the target bit rate distribution is checked, if it is efficient, then it is also the optimal solution; else, optimal bit distribution can be obtained only by few bit swaps. Simulation results using the standard asymmetric digital subscriber line (ADSL test loops show that the proposed algorithm is efficient for practical DMT transmissions.

  17. General conditions for maximal violation of non-contextuality in discrete and continuous variables

    Laversanne-Finot, A.; Ketterer, A.; Barros, M. R.; Walborn, S. P.; Coudreau, T.; Keller, A.; Milman, P.


    The contextuality of quantum mechanics can be shown by the violation of inequalities based on measurements of well chosen observables. An important property of such observables is that their expectation value can be expressed in terms of probabilities for obtaining two exclusive outcomes. Examples of such inequalities have been constructed using either observables with a dichotomic spectrum or using periodic functions obtained from displacement operators in phase space. Here we identify the general conditions on the spectral decomposition of observables demonstrating state independent contextuality of quantum mechanics. Our results not only unify existing strategies for maximal violation of state independent non-contextuality inequalities but also lead to new scenarios enabling such violations. Among the consequences of our results is the impossibility of having a state independent maximal violation of non-contextuality in the Peres–Mermin scenario with discrete observables of odd dimensions.

  18. Subspace Identification

    Knudsen, Torben


    Subspace identification algorithms are user friendly, numerical fast and stable and they provide a good consistent estimate of the deterministic part of a system. The weak point is the stochastic part. The uncertainty on this part is discussed below and methods to reduce it is derived....

  19. Subspace Identification

    Knudsen, Torben


    Subspace identification algorithms are user friendly, numerical fast and stable and they provide a good consistent estimate of the deterministic part of a system. The weak point is the stochastic part. The uncertainty on this part is discussed below and methods to reduce it is derived....

  20. Relevant Subspace Clustering

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan;


    Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace c...

  1. The limiting distribution of the maximal height of the outermost path of nonintersecting Brownian excursions and discrete Gaussian orthogonal polynomials

    Liechty, Karl


    We study the distribution of the maximal height of the outermost path in the model of $N$ nonintersecting Brownian excursions as $N\\to \\infty$, showing that it converges in the proper scaling to the Tracy-Widom distribution for the largest eigenvalue of the Gaussian orthogonal ensemble. This is as expected from the viewpoint that the maximal height of the outermost path converges to the maximum of the Airy process. Our proof is based on Riemann-Hilbert analyis of a system of discrete orthogonal polynomials with a Gaussian weight in the double scaling limit as this system approaches saturation. We consequently compute the asymptotics of the free energy and the reproducing kernel of the corresponding discrete orthogonal polynomial ensemble in the critical scaling in which the density of particles approaches saturation. Both of these results can be viewed as dual to the case in which the mean density of eigenvalues in a random matrix model is vanishing at one point.

  2. OpenSubspace

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan


    Subspace clustering and projected clustering are recent research areas for clustering in high dimensional spaces. As the field is rather young, there is a lack of comparative studies on the advantages and disadvantages of the different algorithms. Part of the underlying problem is the lack...... of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this paper, we discuss the requirements for open source evaluation software. We propose OpenSubspace, an open source framework that meets...... these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in subspace and projected clustering....

  3. Closed Loop Subspace Identification

    Geir W. Nilsen


    Full Text Available A new three step closed loop subspace identifications algorithm based on an already existing algorithm and the Kalman filter properties is presented. The Kalman filter contains noise free states which implies that the states and innovation are uneorre lated. The idea is that a Kalman filter found by a good subspace identification algorithm will give an output which is sufficiently uncorrelated with the noise on the output of the actual process. Using feedback from the output of the estimated Kalman filter in the closed loop system a subspace identification algorithm can be used to estimate an unbiased model.

  4. Subspaces of FMmlet transform

    邹红星; 戴琼海; 赵克; 陈桂明; 李衍达


    The subspaces of FMmlet transform are investigated.It is shown that some of the existing transforms like the Fourier transform,short-time Fourier transform,Gabor transform,wavelet transform,chirplet transform,the mean of signal,and the FM-1let transform,and the butterfly subspace are all special cases of FMmlet transform.Therefore the FMmlet transform is more flexible for delineating both the linear and nonlinear time-varying structures of a signal.

  5. Random subspaces in quantum information theory

    Hayden, Patrick


    The selection of random unitary transformations plays a role in quantum information theory analogous to the role of random hash functions in classical information theory. Recent applications have included protocols achieving the quantum channel capacity and methods for extending superdense coding from bits to qubits. In addition, the corresponding random subspaces have proved useful for studying the structure of bipartite and multipartite entanglement. In quantum information theory, we're fond of saying that Hilbert space is a big place, the implication being that there's room for the unexpected to occur. The goal of this talk is to further bolster this homespun wisdowm. I'm going to present a number of results in quantum information theory that stem from the initially counterintuitive geometry of high-dimensional vector spaces, where subspaces with highly extremal properties are the norm rather than the exception. Peter Shor has shown, for example, that randomly selected subspaces can be used to send quantum information through a noisy quantum channel at the highest possible rate, that is, the quantum channel capacity. More recently, Debbie Leung, Andreas Winter and I demonstrated that a randomly chosen subspace of a bipartite quantum system will likely contain nothing but nearly maximally entangled states, even if the subspace is nearly as large as the original system in qubit terms. This observation has implications for communication, especially superdense coding.

  6. Realistic Decoherence Free Subspaces

    Romero, K M F; Terra-Cunha, M O; Nemes, M C


    Decoherence free subspaces (DFS) is a theoretical tool towards experimental implementation of quantum information storage and processing. However, they represent an experimental challenge, since conditions for their existence are very stringent. This work explores the situation in which a system of $N$ oscillators coupled to a bath of harmonic oscillators is close to satisfy the conditions for the existence of DFS. We show, in the Born-Markov limit and for small deviations from separability and degeneracy conditions, that there are {\\emph{weak decoherence subspaces}} which resemble the original notion of DFS.

  7. A Subspace Algorithm

    Vissing, S.; Hededal, O.

    -dimensional subspace in order to establish and solve a symmetric generalized eigenvalue problem in the subspace. The algorithm is described in pseudo code and implemented in the C programming language for lower triangular matrices A and B. The implementation includes procedures for selecting initial iteration vectors......An algorithm is presented for computing the m smallest eigenvalues and corresponding eigenvectors of the generalized eigenvalue problem (A - λB)Φ = 0 where A and B are real n x n symmetric matrices. In an iteration scheme the matrices A and B are projected simultaneously onto an m...

  8. Operator equations and invariant subspaces

    Valentin Matache


    Full Text Available Banach space operators acting on some fixed space X are considered. If two such operators A and B verify the condition A2=B2 and if A has nontrivial hyperinvariant subspaces, then B has nontrivial invariant subspaces. If A and B commute and satisfy a special type of functional equation, and if A is not a scalar multiple of the identity, the author proves that if A has nontrivial invariant subspaces, then so does B.

  9. Stochastic Homology. Reduction Formulas for Computing Stochastic Betti Numbers of Maximal Random Complexes with Discrete Probabilities. Computation and Applications

    Todorov, Todor


    Given a chain complex with the only modi?cation that each cell of the complex has a probability distribution assigned. We will call this complex - a random complex and what should be understood in practice, is that we have a classical chain complex whose cells appear and disappear according to some probability distributions. In this paper, we will try to fi?nd the stochastic homology of random complex, whose simplices have independent discrete distributions.

  10. Forecasting Using Random Subspace Methods

    T. Boot (Tom); D. Nibbering (Didier)


    textabstractRandom subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two appr

  11. Subspace clustering through attribute clustering

    Kun NIU; Shubo ZHANG; Junliang CHEN


    Many recently proposed subspace clustering methods suffer from two severe problems. First, the algorithms typically scale exponentially with the data dimensionality or the subspace dimensionality of clusters. Second, the clustering results are often sensitive to input parameters. In this paper, a fast algorithm of subspace clustering using attribute clustering is proposed to over-come these limitations. This algorithm first filters out redundant attributes by computing the Gini coefficient. To evaluate the correlation of every two non-redundant attributes, the relation matrix of non-redundant attributes is constructed based on the relation function of two dimensional united Gini coefficients. After applying an overlapping clustering algorithm on the relation matrix, the candidate of all interesting subspaces is achieved. Finally, all subspace clusters can be derived by clustering on interesting subspaces. Experiments on both synthesis and real datasets show that the new algorithm not only achieves a significant gain of runtime and quality to find subspace clusters, but also is insensitive to input parameters.

  12. Krylov subspace method based on data preprocessing technology


    The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest (SOI) component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom (DOF) of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.


    生汉芳; 刘新国


    This paper proposes the Rice condition numbers for invariant subspace, singular subspaces of a matrix and deflating subspaces of a regular matrix pair. The first-order perturbation estimations for these subspaces are derived by applying perturbation expansions of orthogonal projection operators.

  14. Average sampling theorems for shift invariant subspaces


    The sampling theorem is one of the most powerful results in signal analysis. In this paper, we study the average sampling on shift invariant subspaces, e.g. wavelet subspaces. We show that if a subspace satisfies certain conditions, then every function in the subspace is uniquely determined and can be reconstructed by its local averages near certain sampling points. Examples are given.

  15. Propagator-based methods for recursive subspace model identification

    Mercère, Guillaume; Bako, Laurent; Lecoeuche, Stéphane


    International audience; The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive formulation of the MIMO output error state-space (MOESP) identification class, are developed. The main goals of the proposed methods are to circumvent the huge complexity of eigenvalues or singular values decomposition techniques used by the offline algorit...

  16. Shape analysis with subspace symmetries

    Berner, Alexander


    We address the problem of partial symmetry detection, i.e., the identification of building blocks a complex shape is composed of. Previous techniques identify parts that relate to each other by simple rigid mappings, similarity transforms, or, more recently, intrinsic isometries. Our approach generalizes the notion of partial symmetries to more general deformations. We introduce subspace symmetries whereby we characterize similarity by requiring the set of symmetric parts to form a low dimensional shape space. We present an algorithm to discover subspace symmetries based on detecting linearly correlated correspondences among graphs of invariant features. We evaluate our technique on various data sets. We show that for models with pronounced surface features, subspace symmetries can be found fully automatically. For complicated cases, a small amount of user input is used to resolve ambiguities. Our technique computes dense correspondences that can subsequently be used in various applications, such as model repair and denoising. © 2010 The Author(s).

  17. Generalized subspace correction methods

    Kolm, P. [Royal Institute of Technology, Stockholm (Sweden); Arbenz, P.; Gander, W. [Eidgenoessiche Technische Hochschule, Zuerich (Switzerland)


    A fundamental problem in scientific computing is the solution of large sparse systems of linear equations. Often these systems arise from the discretization of differential equations by finite difference, finite volume or finite element methods. Iterative methods exploiting these sparse structures have proven to be very effective on conventional computers for a wide area of applications. Due to the rapid development and increasing demand for the large computing powers of parallel computers, it has become important to design iterative methods specialized for these new architectures.

  18. Robust adaptive subspace detection in impulsive noise

    Atitallah, Ismail Ben


    This paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.

  19. Scalable Density-Based Subspace Clustering

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan;


    For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering ...

  20. Subspace K-means clustering

    Timmerman, Marieke E.; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla


    To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existi

  1. Subspace Methods for Eigenvalue Problems

    Hochstenbach, Michiel Erik


    This thesis treats a number of aspects of subspace methods for various eigenvalue problems. Vibrations and their corresponding eigenvalues (or frequencies) arise in science, engineering, and daily life. Matrix eigenvalue problems come from a large number of areas, such as chemistry, mechanics, dyn

  2. Skyline View: Efficient Distributed Subspace Skyline Computation

    Kim, Jinhan; Lee, Jongwuk; Hwang, Seung-Won

    Skyline queries have gained much attention as alternative query semantics with pros (e.g.low query formulation overhead) and cons (e.g.large control over result size). To overcome the cons, subspace skyline queries have been recently studied, where users iteratively specify relevant feature subspaces on search space. However, existing works mainly focuss on centralized databases. This paper aims to extend subspace skyline computation to distributed environments such as the Web, where the most important issue is to minimize the cost of accessing vertically distributed objects. Toward this goal, we exploit prior skylines that have overlapped subspaces to the given subspace. In particular, we develop algorithms for three scenarios- when the subspace of prior skylines is superspace, subspace, or the rest. Our experimental results validate that our proposed algorithm shows significantly better performance than the state-of-the-art algorithms.

  3. Conjugate Gradient with Subspace Optimization

    Karimi, Sahar


    In this paper we present a variant of the conjugate gradient (CG) algorithm in which we invoke a subspace minimization subproblem on each iteration. We call this algorithm CGSO for "conjugate gradient with subspace optimization". It is related to earlier work by Nemirovsky and Yudin. We apply the algorithm to solve unconstrained strictly convex problems. As with other CG algorithms, the update step on each iteration is a linear combination of the last gradient and last update. Unlike some other conjugate gradient methods, our algorithm attains a theoretical complexity bound of $O(\\sqrt{L/l} \\log(1/\\epsilon))$, where the ratio $L/l$ characterizes the strong convexity of the objective function. In practice, CGSO competes with other CG-type algorithms by incorporating some second order information in each iteration.

  4. Subspace learning of neural networks

    Cheng Lv, Jian; Zhou, Jiliu


    PrefaceChapter 1. Introduction1.1 Introduction1.1.1 Linear Neural Networks1.1.2 Subspace Learning1.2 Subspace Learning Algorithms1.2.1 PCA Learning Algorithms1.2.2 MCA Learning Algorithms1.2.3 ICA Learning Algorithms1.3 Methods for Convergence Analysis1.3.1 SDT Method1.3.2 DCT Method1.3.3 DDT Method1.4 Block Algorithms1.5 Simulation Data Set and Notation1.6 ConclusionsChapter 2. PCA Learning Algorithms with Constants Learning Rates2.1 Oja's PCA Learning Algorithms2.1.1 The Algorithms2.1.2 Convergence Issue2.2 Invariant Sets2.2.1 Properties of Invariant Sets2.2.2 Conditions for Invariant Sets2.

  5. Subspace Arrangement Codes and Cryptosystems


    Signature Date Acceptance for the Trident Scholar Committee Professor Carl E. Wick Associate Director of Midshipmen Research Signature Date SUBSPACE...Professor William Traves. I also thank Professor Carl Wick and the Trident Scholar Committee for providing me with the opportunity to conduct this... Sagan . Why the characteristic polynomial factors. Bulletin of the American Mathematical Society, 36(2):113–133, February 1999. [16] Karen E. Smith

  6. Random matrix improved subspace clustering

    Couillet, Romain


    This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based on vector channel observations in the context of massive MIMO wireless communication networks is provided.


    Li Qiang; Qiu Zhengding; Sun Dongmei


    In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person,we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy.

  8. Incremental Supervised Subspace Learning for Face Recognition


    Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets.

  9. Kernel based subspace projection of hyperspectral images

    Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten

    In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF...

  10. On Subspaces of an Almost -Lagrange Space

    P. N. Pandey


    Full Text Available We discuss the subspaces of an almost -Lagrange space (APL space in short. We obtain the induced nonlinear connection, coefficients of coupling, coefficients of induced tangent and induced normal connections, the Gauss-Weingarten formulae, and the Gauss-Codazzi equations for a subspace of an APL-space. Some consequences of the Gauss-Weingarten formulae have also been discussed.

  11. Subspace learning from image gradient orientations

    Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja


    We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the l

  12. Jacobi method for signal subspace computation

    Paul, Steffen; Goetze, Juergen


    The Jacobi method for singular value decomposition is well-suited for parallel architectures. Its application to signal subspace computations is well known. Basically the subspace spanned by singular vectors of large singular values are separated from subspace spanned by those of small singular values. The Jacobi algorithm computes the singular values and the corresponding vectors in random order. This requires sorting the result after convergence of the algorithm to select the signal subspace. A modification of the Jacobi method based on a linear objective function merges the sorting into the SVD-algorithm at little extra cost. In fact, the complexity of the diagonal processor cells in a triangular array get slightly larger. In this paper we present these extensions, in particular the modified algorithm for computing the rotation angles and give an example of its usefulness for subspace separation.

  13. Extended Krylov subspaces approximations of matrix functions. Application to computational electromagnetics

    Druskin, V.; Lee, Ping [Schlumberger-Doll Research, Ridgefield, CT (United States); Knizhnerman, L. [Central Geophysical Expedition, Moscow (Russian Federation)


    There is now a growing interest in the area of using Krylov subspace approximations to compute the actions of matrix functions. The main application of this approach is the solution of ODE systems, obtained after discretization of partial differential equations by method of lines. In the event that the cost of computing the matrix inverse is relatively inexpensive, it is sometimes attractive to solve the ODE using the extended Krylov subspaces, originated by actions of both positive and negative matrix powers. Examples of such problems can be found frequently in computational electromagnetics.

  14. Explaining outliers by subspace separability

    Micenková, Barbora; Ng, Raymond T.; Dang, Xuan-Hong


    Outliers are extraordinary objects in a data collection. Depending on the domain, they may represent errors, fraudulent activities or rare events that are subject of our interest. Existing approaches focus on detection of outliers or degrees of outlierness (ranking), but do not provide a possible...... explanation of how these objects deviate from the rest of the data. Such explanations would help user to interpret or validate the detected outliers. The problem addressed in this paper is as follows: given an outlier detected by an existing algorithm, we propose a method that determines possible explanations...... for the outlier. These explanations are expressed in the form of subspaces in which the given outlier shows separability from the inliers. In this manner, our proposed method complements existing outlier detection algorithms by providing additional information about the outliers. Our method is designed to work...

  15. Maximally informative dimensions Analyzing neural responses to natural signals

    Sharpee, T; Bialek, W; Sharpee, Tatyana; Rust, Nicole C.; Bialek, William


    We propose a method that would allow for a rigorous statistical analysis of neural responses to natural stimuli, which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of the high dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of Gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed i...

  16. Analyzing neural responses to natural signals maximally informative dimensions

    Sharpee, T; Bialek, W; Sharpee, Tatyana; Rust, Nicole C.; Bialek, William


    We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of a high dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of Gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small,...

  17. The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspace.

    Wang, Bing; Mueller, Klaus


    Analyzing high-dimensional data and finding hidden patterns is a difficult problem and has attracted numerous research efforts. Automated methods can be useful to some extent but bringing the data analyst into the loop via interactive visual tools can help the discovery process tremendously. An inherent problem in this effort is that humans lack the mental capacity to truly understand spaces exceeding three spatial dimensions. To keep within this limitation, we describe a framework that decomposes a high-dimensional data space into a continuum of generalized 3D subspaces. Analysts can then explore these 3D subspaces individually via the familiar trackball interface while using additional facilities to smoothly transition to adjacent subspaces for expanded space comprehension. Since the number of such subspaces suffers from combinatorial explosion, we provide a set of data-driven subspace selection and navigation tools which can guide users to interesting subspaces and views. A subspace trail map allows users to manage the explored subspaces, keep their bearings, and return to interesting subspaces and views. Both trackball and trail map are each embedded into a word cloud of attribute labels which aid in navigation. We demonstrate our system via several use cases in a diverse set of application areas - cluster analysis and refinement, information discovery, and supervised training of classifiers. We also report on a user study that evaluates the usability of the various interactions our system provides.

  18. Image Deblurring with Krylov Subspace Methods

    Hansen, Per Christian


    Image deblurring, i.e., reconstruction of a sharper image from a blurred and noisy one, involves the solution of a large and very ill-conditioned system of linear equations, and regularization is needed in order to compute a stable solution. Krylov subspace methods are often ideally suited...... for this task: their iterative nature is a natural way to handle such large-scale problems, and the underlying Krylov subspace provides a convenient mechanism to regularized the problem by projecting it onto a low-dimensional "signal subspace" adapted to the particular problem. In this talk we consider...... the three Krylov subspace methods CGLS, MINRES, and GMRES. We describe their regularizing properties, and we discuss some computational aspects such as preconditioning and stopping criteria....

  19. Alternating Krylov subspace image restoration methods

    Abad, J.O; Morigi, S; Reichel, L; Sgallari, F


    ... of the Krylov subspace used. However, our solution methods, suitably modified, also can be applied when no bound for the norm of η δ is known. We determine an approximation of the desired image u ˆ by so...

  20. Sinusoidal Order Estimation Using Angles between Subspaces

    Søren Holdt Jensen


    Full Text Available We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods.

  1. Face recognition with L1-norm subspaces

    Maritato, Federica; Liu, Ying; Colonnese, Stefania; Pados, Dimitris A.


    We consider the problem of representing individual faces by maximum L1-norm projection subspaces calculated from available face-image ensembles. In contrast to conventional L2-norm subspaces, L1-norm subspaces are seen to offer significant robustness to image variations, disturbances, and rank selection. Face recognition becomes then the problem of associating a new unknown face image to the "closest," in some sense, L1 subspace in the database. In this work, we also introduce the concept of adaptively allocating the available number of principal components to different face image classes, subject to a given total number/budget of principal components. Experimental studies included in this paper illustrate and support the theoretical developments.

  2. Kernel based subspace projection of hyperspectral images

    Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten

    In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF). Th......). The MAF projection exploits the fact that interesting phenomena in images typically exhibit spatial autocorrelation. The analysis is based on nearinfrared hyperspectral images of maize grains demonstrating the superiority of the kernelbased MAF method....

  3. An extended EM algorithm for subspace clustering

    Lifei CHEN; Qingshan JIANG


    Clustering high dimensional data has become a challenge in data mining due to the curse of dimension-ality. To solve this problem, subspace clustering has been defined as an extension of traditional clustering that seeks to find clusters in subspaces spanned by different combinations of dimensions within a dataset. This paper presents a new subspace clustering algorithm that calcu-lates the local feature weights automatically in an EM-based clustering process. In the algorithm, the features are locally weighted by using a new unsupervised weight-ing method, as a means to minimize a proposed cluster-ing criterion that takes into account both the average intra-clusters compactness and the average inter-clusters separation for subspace clustering. For the purposes of capturing accurate subspace information, an additional outlier detection process is presented to identify the pos-sible local outliers of subspace clusters, and is embedded between the E-step and M-step of the algorithm. The method has been evaluated in clustering real-world gene expression data and high dimensional artificial data with outliers, and the experimental results have shown its effectiveness.

  4. Modeling Non-Equilibrium Dynamics of a Discrete Probability Distribution: General Rate Equation for Maximal Entropy Generation in a Maximum-Entropy Landscape with Time-Dependent Constraints

    Gian Paolo Beretta


    Full Text Available A rate equation for a discrete probability distribution is discussed as a route to describe smooth relaxation towards the maximum entropy distribution compatible at all times with one or more linear constraints. The resulting dynamics follows the path of steepest entropy ascent compatible with the constraints. The rate equation is consistent with the Onsager theorem of reciprocity and the fluctuation-dissipation theorem. The mathematical formalism was originally developed to obtain a quantum theoretical unification of mechanics and thermodinamics. It is presented here in a general, non-quantal formulation as a part of an effort to develop tools for the phenomenological treatment of non-equilibrium problems with applications in engineering, biology, sociology, and economics. The rate equation is also extended to include the case of assigned time-dependences of the constraints and the entropy, such as for modeling non-equilibrium energy and entropy exchanges.

  5. Modeling Non-Equilibrium Dynamics of a Discrete Probability Distribution: General Rate Equation for Maximal Entropy Generation in a Maximum-Entropy Landscape with Time-Dependent Constraints

    Beretta, Gian P.


    A rate equation for a discrete probability distribution is discussed as a route to describe smooth relaxation towards the maximum entropy distribution compatible at all times with one or more linear constraints. The resulting dynamics follows the path of steepest entropy ascent compatible with the constraints. The rate equation is consistent with the Onsager theorem of reciprocity and the fluctuation-dissipation theorem. The mathematical formalism was originally developed to obtain a quantum theoretical unification of mechanics and thermodinamics. It is presented here in a general, non-quantal formulation as a part of an effort to develop tools for the phenomenological treatment of non-equilibrium problems with applications in engineering, biology, sociology, and economics. The rate equation is also extended to include the case of assigned time-dependences of the constraints and the entropy, such as for modeling non-equilibrium energy and entropy exchanges.

  6. 4DVar Assimilation in the Unstable Subspace : existence of an optimal subspace dimension

    Trevisan, Anna; D'Isidoro, Massimo; Talagrand, Olivier


    The nonlinear stability properties of a chaotic system are exploited to formulate a reduced subspace 4-dimensional assimilation algorithm, 4DVar-AUS (Assimilation in the Unstable Subspace). The key result is the existence of an optimal subspace dimension for the assimilation that is directly related to the unstable subspace dimension. Theoretical arguments suggest that the optimal subspace dimension is equal to N++1, where N+ is the number of nonnegative Lyapunov exponents. In support of the theory, numerical experiments are performed in a simple model with a variable number of positive exponents: the results show that, in the presence of observational error, the confinement of the assimilation increment in the unstable subspace of the system reduces the RMS analysis error with respect to standard 4DVar. The standard 4DVar solution, while being closer to the observations, is further away from the truth. The explanation of this result is that, assimilating in the unstable subspace, errors in the stable directions are naturally damped: because of observational error, assimilating the whole space otherwise prevents this decay. In agreement with this interpretation, if observations are perfect standard 4DVar gives the best results. These results are in agreement with an independent theoretical study of the Extended Kalman Filter, which show that the error concentrates in the unstable subspace.

  7. Subspace accelerated inexact Newton method for large scale wave functions calculations in Density Functional Theory

    Fattebert, J


    We describe an iterative algorithm to solve electronic structure problems in Density Functional Theory. The approach is presented as a Subspace Accelerated Inexact Newton (SAIN) solver for the non-linear Kohn-Sham equations. It is related to a class of iterative algorithms known as RMM-DIIS in the electronic structure community. The method is illustrated with examples of real applications using a finite difference discretization and multigrid preconditioning.

  8. Monomial codes seen as invariant subspaces

    García-Planas María Isabel


    Full Text Available It is well known that cyclic codes are very useful because of their applications, since they are not computationally expensive and encoding can be easily implemented. The relationship between cyclic codes and invariant subspaces is also well known. In this paper a generalization of this relationship is presented between monomial codes over a finite field and hyperinvariant subspaces of n under an appropriate linear transformation. Using techniques of Linear Algebra it is possible to deduce certain properties for this particular type of codes, generalizing known results on cyclic codes.

  9. Matrix Krylov subspace methods for image restoration

    khalide jbilou


    Full Text Available In the present paper, we consider some matrix Krylov subspace methods for solving ill-posed linear matrix equations and in those problems coming from the restoration of blurred and noisy images. Applying the well known Tikhonov regularization procedure leads to a Sylvester matrix equation depending the Tikhonov regularized parameter. We apply the matrix versions of the well known Krylov subspace methods, namely the Least Squared (LSQR and the conjugate gradient (CG methods to get approximate solutions representing the restored images. Some numerical tests are presented to show the effectiveness of the proposed methods.

  10. Comparing Subspace Methods for Closed Loop Subspace System Identification by Monte Carlo Simulations

    David Di Ruscio


    Full Text Available A novel promising bootstrap subspace system identification algorithm for both open and closed loop systems is presented. An outline of the SSARX algorithm by Jansson (2003 is given and a modified SSARX algorithm is presented. Some methods which are consistent for closed loop subspace system identification presented in the literature are discussed and compared to a recently published subspace algorithm which works for both open as well as for closed loop data, i.e., the DSR_e algorithm as well as the bootstrap method. Experimental comparisons are performed by Monte Carlo simulations.

  11. Bounds on Subspace Codes Based on Subspaces of Type (m,1 in Singular Linear Space

    You Gao


    Full Text Available The Sphere-packing bound, Singleton bound, Wang-Xing-Safavi-Naini bound, Johnson bound, and Gilbert-Varshamov bound on the subspace codes n+l,M,d,(m,1q based on subspaces of type (m,1 in singular linear space Fq(n+l over finite fields Fq are presented. Then, we prove that codes based on subspaces of type (m,1 in singular linear space attain the Wang-Xing-Safavi-Naini bound if and only if they are certain Steiner structures in Fq(n+l.

  12. Spatial Bell-State Generation without Transverse Mode Subspace Postselection

    Kovlakov, E. V.; Bobrov, I. B.; Straupe, S. S.; Kulik, S. P.


    Spatial states of single photons and spatially entangled photon pairs are becoming an important resource in quantum communication. This additional degree of freedom provides an almost unlimited information capacity, making the development of high-quality sources of spatial entanglement a well-motivated research direction. We report an experimental method for generation of photon pairs in a maximally entangled spatial state. In contrast to existing techniques, the method does not require postselection of a particular subspace of spatial modes and allows one to use the full photon flux from the nonlinear crystal, providing a tool for creating high-brightness sources of pure spatially entangled photons. Such sources are a prerequisite for emerging applications in free-space quantum communication.

  13. Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation

    Qiao Bi


    Full Text Available A formalism for quantum computing in decoherence-free subspaces is presented. The constructed subspaces are partial triangulated to an index related to environment. The quantum states in the subspaces are just projected states which are ruled by a subdynamic kinetic equation. These projected states can be used to perform ideal quantum logical operations without decoherence.

  14. Biomarkers spectral subspace for cancer detection.

    Sun, Yi; Pu, Yang; Yang, Yuanlong; Alfano, Robert R


    A novel approach to cancer detection in biomarkers spectral subspace (BSS) is proposed. The basis spectra of the subspace spanned by fluorescence spectra of biomarkers are obtained by the Gram-Schmidt method. A support vector machine classifier (SVM) is trained in the subspace. The spectrum of a sample tissue is projected onto and is classified in the subspace. In addition to sensitivity and specificity, the metrics of positive predictivity, Score1, maximum Score1, and accuracy (AC) are employed for performance evaluation. The proposed BSS using SVM is applied to breast cancer detection using four biomarkers: collagen, NADH, flavin, and elastin, with 340-nm excitation. It is found that the BSS SVM outperforms the approach based on multivariate curve resolution (MCR) using SVM and achieves the best performance of principal component analysis (PCA) using SVM among all combinations of PCs. The descent order of efficacy of the four biomarkers in the breast cancer detection of this experiment is collagen, NADH, elastin, and flavin. The advantage of BSS is twofold. First, all diagnostically useful information of biomarkers for cancer detection is retained while dimensionality of data is significantly reduced to obviate the curse of dimensionality. Second, the efficacy of biomarkers in cancer detection can be determined.

  15. Subspace System Identification of the Kalman Filter

    David Di Ruscio


    Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.

  16. Index formulae for subspaces of Krein spaces

    Dijksma, A; Gheondea, A


    For a subspace S of a Krein space K and an arbitrary fundamental decomposition K = K-[+]K+ of K, we prove the index formula k(-)(S) + dim(S-perpendicular to boolean AND K-+) = k(+)(S-perpendicular to)+ dim(S boolean AND K-), were k(+/-)(S) stands for the positive/negative signature of S. The differe

  17. Interference subspace rejection in wideband CDMA:

    Hansen, Henrik; Affes, Sofiene; Mermelstein, Paul


    This paper extends our study on a multi-user receiver structure for base-station receivers with antenna arrays in multicellular systems. The receiver employs a beamforming structure with constraints that nulls the signal component in appropriate interference subspaces. Here we introduce a new mod...

  18. Partial interference subspace rejection in CDMA systems

    Hansen, Henrik; Affes, Sofiene; Mewelstein, Paul


    Previously presented interference subspace rejection (ISR) proposed a family of new efficient multiuser detectors for CDMA. We reconsider in this paper the modes of ISR using decision feedback (DF). DF modes share similarities with parallel interference cancellation (PIC) but attempt to cancel...

  19. Robust Latent Subspace Learning for Image Classification.

    Fang, Xiaozhao; Teng, Shaohua; Lai, Zhihui; He, Zhaoshui; Xie, Shengli; Wong, Wai Keung


    This paper proposes a novel method, called robust latent subspace learning (RLSL), for image classification. We formulate an RLSL problem as a joint optimization problem over both the latent SL and classification model parameter predication, which simultaneously minimizes: 1) the regression loss between the learned data representation and objective outputs and 2) the reconstruction error between the learned data representation and original inputs. The latent subspace can be used as a bridge that is expected to seamlessly connect the origin visual features and their class labels and hence improve the overall prediction performance. RLSL combines feature learning with classification so that the learned data representation in the latent subspace is more discriminative for classification. To learn a robust latent subspace, we use a sparse item to compensate error, which helps suppress the interference of noise via weakening its response during regression. An efficient optimization algorithm is designed to solve the proposed optimization problem. To validate the effectiveness of the proposed RLSL method, we conduct experiments on diverse databases and encouraging recognition results are achieved compared with many state-of-the-arts methods.

  20. Joint Local Quasinilpotence and Common Invariant Subspaces

    A Fernández Valles


    In this article we obtain some positive results about the existence of a common nontrivial invariant subspace for -tuples of not necessarily commuting operators on Banach spaces with a Schauder basis. The concept of joint quasinilpotence plays a basic role. Our results complement recent work by Kosiek [6] and Ptak [8].

  1. Compressive Detection of Random Subspace Signals

    Razavi, Alireza; Valkama, Mikko; Cabric, Danijela


    The problem of compressive detection of random subspace signals is studied. We consider signals modeled as $\\mathbf{s} = \\mathbf{H} \\mathbf{x}$ where $\\mathbf{H}$ is an $N \\times K$ matrix with $K \\le N$ and $\\mathbf{x} \\sim \\mathcal{N}(\\mathbf{0}_{K,1},\\sigma_x^2 \\mathbf{I}_K)$. We say that signal $\\mathbf{s}$ lies in or leans toward a subspace if the largest eigenvalue of $\\mathbf{H} \\mathbf{H}^T$ is strictly greater than its smallest eigenvalue. We first design a measurement matrix $\\mathbf{\\Phi}=[\\mathbf{\\Phi}_s^T,\\mathbf{\\Phi}_o^T]^T$ comprising of two sub-matrices $\\mathbf{\\Phi}_s$ and $\\mathbf{\\Phi}_o$ where $\\mathbf{\\Phi}_s$ projects the signals to the strongest left-singular vectors, i.e., the left-singular vectors corresponding to the largest singular values, of subspace matrix $\\mathbf{H}$ and $\\mathbf{\\Phi}_o$ projects it to the weakest left-singular vectors. We then propose two detectors which work based on the difference in energies of the samples measured by two sub-matrices $\\mathbf{\\Phi}_s$ and $\\mathbf{\\Phi}_o$ and prove their optimality. Simplified versions of the proposed detectors for the case when the variance of noise is known are also provided. Furthermore, we study the performance of the detector when measurements are imprecise and show how imprecision can be compensated by employing more measurement devices. The problem is then re-formulated for the case when the signal lies in the union of a finite number of linear subspaces instead of a single linear subspace. Finally, we study the performance of the proposed methods by simulation examples.

  2. A Subspace Approach to Blind Multiuser Detection for Ultra-Wideband Communication Systems

    Liu Ping


    Full Text Available Impulse radio-based ultra-wideband (UWB communication systems allow multiple users to access channels simultaneously by assigning unique time-hopping codes to individual users, while each user's information stream is modulated by pulse-position modulation (PPM. However, transmitted signals undergo fading from a number of propagation paths in a dense multipath environment and meanwhile suffer from multiuser interference (MUI. Although RAKE receiver can be employed to maximally exploit path diversity, it is a single-user receiver. Multiuser receiver can significantly improve detection performance. Each of these receivers requires channel parameters. Existing maximum likelihood channel estimators treat MUI as Gaussian noise. In this paper, we derive a blind subspace channel estimator first and then design linear receivers. Following a channel input/output model that transforms a PPM signal into a sum of seemingly pulse-amplitude modulated signals, a structure similar to a code-division multiple-access (CDMA system is observed. Code matrices for each user are identified. After considering unique statistical properties of new inputs such as mean and covariance, the model is further transformed to ensure that all signature waveforms lie in the signal subspace and are orthogonal to the noise subspace. Consequently, a subspace technique is applicable to estimate each channel. Then minimum mean square error receivers of two different versions are designed, suitable for both uplink and downlink. Asymptotic performance of both the channel estimator and receivers is studied. Closed-form bit error rate is also derived.

  3. Explicit and implicit ode solvers using Krylov subspace optimization: Application to the diffusion equation and parabolic Maxwell`s system

    Druskin, V.; Knizhnerman, L.


    The authors solve the Cauchy problem for an ODE system Au + {partial_derivative}u/{partial_derivative}t = 0, u{vert_bar}{sub t=0} = {var_phi}, where A is a square real nonnegative definite symmetric matrix of the order N, {var_phi} is a vector from R{sup N}. The stiffness matrix A is obtained due to semi-discretization of a parabolic equation or system with time-independent coefficients. The authors are particularly interested in large stiff 3-D problems for the scalar diffusion and vectorial Maxwell`s equations. First they consider an explicit method in which the solution on a whole time interval is projected on a Krylov subspace originated by A. Then they suggest another Krylov subspace with better approximating properties using powers of an implicit transition operator. These Krylov subspace methods generate optimal in a spectral sense polynomial approximations for the solution of the ODE, similar to CG for SLE.

  4. Engineering Stable Discrete-Time Quantum Dynamics via a Canonical QR Decomposition

    Bolognani, Saverio


    We analyze the asymptotic behavior of discrete-time, Markovian quantum systems with respect to a subspace of interest. Global asymptotic stability of subspaces is relevant to quantum information processing, in particular for initializing the system in pure states or subspace codes. We provide a linear-algebraic characterization of the dynamical properties leading to invariance and attractivity of a given quantum subspace. We then construct a design algorithm for discrete-time feedback control that allows to stabilize a target subspace, proving that if the control problem is feasible, then the algorithm returns an effective control choice. In order to prove this result, a canonical QR matrix decomposition is derived, and also used to establish the control scheme potential for the simulation of open-system dynamics.

  5. Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees

    Ni, Yuzhao; Yuan, Xiaotong; Yan, Shuicheng; Cheong, Loong-Fah


    Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group) {Throughout the paper, we use segmentation, clustering, and grouping, and their verb forms, interchangeably.} high-dimensional structural data such as those (approximately) lying on subspaces {We follow \\cite{liu2010robust} and use the term "subspace" to denote both linear subspaces and affine subspaces. There is a trivial conversion between linear subspaces and affine subspaces as mentioned therein.} or low-dimensional manifolds. By learning the affinity matrix in the form of sparse reconstruction, techniques proposed in this vein often considerably boost the performance in subspace settings where traditional SC can fail. Despite the success, there are fundamental problems that have been left unsolved: the spectrum property of the learned affinity matrix cannot be gauged in advance, and there is often one ugly symmetrization step that post-processes the affinity for SC input. Hence we advocate to enforce...

  6. Maximal trees

    Brendle, Joerg


    We show that, consistently, there can be maximal subtrees of P (omega) and P (omega) / fin of arbitrary regular uncountable size below the size of the continuum. We also show that there are no maximal subtrees of P (omega) / fin with countable levels. Our results answer several questions of Campero, Cancino, Hrusak, and Miranda.

  7. Subspace identification for continuous-time errors-in-variables model from sampled data

    Ping WU; Chun-jie YANG; Zhi-huan SONG


    We study the subspace identification for the continuous-time errors-in-variables model from sampled data. First, the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identification. The generalized Poisson moment functional is focused. A total least squares equation based on this filtering approach is derived. Inspired by the idea of discrete-time subspace identification based on principal component analysis, we develop two algorithms to deliver consistent estimates for the continuous-time errors-in-variables model by introducing two different instrumental variables. Order determination and other instrumental variables are discussed. The usefulness of the proposed algorithms is illustrated through numerical simulation.

  8. On a subspace of dual Zariski topology

    Ćeken, Seçil


    Let R be a commutative ring with identity and S pecs(M) (resp. Min(M)) denote the set of all second (resp. minimal) submodules of a non-zero R-module M. In this paper, we investigate several properties of the subspace topology on Min(M) induced by the dual Zariski on S pecs(M) and determine some cases in which Min(M) is a max-spectral space.

  9. Maximizing Entropy over Markov Processes

    Biondi, Fabrizio; Legay, Axel; Nielsen, Bo Friis


    computation reduces to finding a model of a specification with highest entropy. Entropy maximization for probabilistic process specifications has not been studied before, even though it is well known in Bayesian inference for discrete distributions. We give a characterization of global entropy of a process...... as a reward function, a polynomial algorithm to verify the existence of an system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy. We show how...

  10. Maximizing entropy over Markov processes

    Biondi, Fabrizio; Legay, Axel; Nielsen, Bo Friis


    computation reduces to finding a model of a specification with highest entropy. Entropy maximization for probabilistic process specifications has not been studied before, even though it is well known in Bayesian inference for discrete distributions. We give a characterization of global entropy of a process...... as a reward function, a polynomial algorithm to verify the existence of a system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy. We show how...

  11. New learning subspace method for image feature extraction

    CAO Jian-hai; LI Long; LU Chang-hou


    A new method of Windows Minimum/Maximum Module Learning Subspace Algorithm(WMMLSA) for image feature extraction is presented. The WMMLSM is insensitive to the order of the training samples and can regulate effectively the radical vectors of an image feature subspace through selecting the study samples for subspace iterative learning algorithm,so it can improve the robustness and generalization capacity of a pattern subspace and enhance the recognition rate of a classifier. At the same time,a pattern subspace is built by the PCA method. The classifier based on WMMLSM is successfully applied to recognize the pressed characters on the gray-scale images. The results indicate that the correct recognition rate on WMMLSM is higher than that on Average Learning Subspace Method,and that the training speed and the classification speed are both improved. The new method is more applicable and efficient.

  12. Invariant Subspaces of the Two-Dimensional Nonlinear Evolution Equations

    Chunrong Zhu


    Full Text Available In this paper, we develop the symmetry-related methods to study invariant subspaces of the two-dimensional nonlinear differential operators. The conditional Lie–Bäcklund symmetry and Lie point symmetry methods are used to construct invariant subspaces of two-dimensional differential operators. We first apply the multiple conditional Lie–Bäcklund symmetries to derive invariant subspaces of the two-dimensional operators. As an application, the invariant subspaces for a class of two-dimensional nonlinear quadratic operators are provided. Furthermore, the invariant subspace method in one-dimensional space combined with the Lie symmetry reduction method and the change of variables is used to obtain invariant subspaces of the two-dimensional nonlinear operators.

  13. Indoor Subspacing to Implement Indoorgml for Indoor Navigation

    Jung, H.; Lee, J.


    According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.


    H. Jung


    Full Text Available According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.

  15. Computing approximate (symmetric block) rational Krylov subspaces without explicit inversion

    Mach, Thomas; Pranić, Miroslav S.; Vandebril, Raf


    It has been shown, see TW623, that approximate extended Krylov subspaces can be computed —under certain assumptions— without any explicit inversion or system solves. Instead the necessary products A-1v are obtained in an implicit way retrieved from an enlarged Krylov subspace. In this paper this approach is generalized to rational Krylov subspaces, which contain besides poles at infinite and zero also finite non-zero poles. Also an adaption of the algorithm to the block and the symmetric ...

  16. Entropy Maximization

    K B Athreya


    It is shown that (i) every probability density is the unique maximizer of relative entropy in an appropriate class and (ii) in the class of all pdf that satisfy $\\int fh_id_=_i$ for $i=1,2,\\ldots,\\ldots k$ the maximizer of entropy is an $f_0$ that is proportional to $\\exp(\\sum c_i h_i)$ for some choice of $c_i$. An extension of this to a continuum of constraints and many examples are presented.


    Liu Yulin; Peng Qicong


    A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.


    LiuYulin; PengQicong


    A new blind method is proposed for identification of CDMA Time-Varyin(TV) channels in this paper.By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant(TI) coordingates is proposed.Unlike existing basis expansion methods, this new algorithm does not require estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols.The algorithm offers definite explanation of the expansion coordinates.Simulation demonstrates the effectiveness of the algorithm.

  19. Subspace decomposition-based correlation matrix multiplication

    Cheng Hao; Guo Wei; Yu Jingdong


    The correlation matrix, which is widely used in eigenvalue decomposition (EVD) or singular value decomposition (SVD), usually can be denoted by R = E[yiy'i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum (DSSS) signal's signature sequence estimation.The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.

  20. Reliability-based concurrent subspace optimization method

    FAN Hui; LI Wei-ji


    To avoid the high computational cost and much modification in the process of applying traditional re-liability-based design optimization method, a new reliability-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing muhidisciplinary optimization techniques and reli-ability assessment methods. It is shown through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current de-terministic optimization process.

  1. Subspace Signal Processing in Structured Noise


    subspace spanned by a matrix of the form: :0 .. .0 Hrt (2 .1) Such a matrLx is called a Vandermonde matriz when m = n [GVL89]. We follow Demeure [Dem89] in...of Squared Bias. We now consider how he squared bias may bc estimated from the data for a given low rank projection P. Reca!l h!nt ie squared !ia is...mathematics used here is not new. Since it is mostly of a linear algebraic nature, it can be found in such books as the classic Matriz Compufalzons by Golub

  2. The Generalised Discrete Algebraic Riccati Equation in LQ optimal control

    Ferrante, Augusto


    This paper investigates the properties of the solutions of the generalised discrete algebraic Riccati equation arising from the solution of the classic infinite-horizon linear quadratic control problem. In particular, a geometric analysis is used to study the relationship existing between the solutions of the generalised Riccati equation and the output-nulling subspaces of the underlying system and the corresponding reachability subspaces. This analysis reveals the presence of a subspace that plays an important role in the solution of the related optimal control problem, which is reflected in the generalised eigenstructure of the corresponding extended symplectic pencil. In establishing themain results of this paper, several ancillay problems on the discrete Lyapunov equation and spectral factorisation are also addressed and solved.

  3. A simple subspace approach for speech denoising.

    Manfredi, C; Daniello, M; Bruscaglioni, P


    For pathological voices, hoarseness is mainly due to airflow turbulence in the vocal tract and is often referred to as noise. This paper focuses on the enhancement of speech signals that are supposedly degraded by additive white noise. Speech enhancement is performed in the time-domain, by means of a fast and reliable subspace approach. A low-order singular value decomposition (SVD) allows separating the signal and the noise contribution in subsequent data frames of the analysed speech signal. The noise component is thus removed from the signal and the filtered signal is reconstructed along the directions spanned by the eigenvectors associated with the signal subspace eigenvalues only, thus giving enhanced voice quality. This approach was tested on synthetic data, showing higher performance in terms of increased SNR when compared with linear prediction (LP) filtering. It was also successfully applied to real data, from hoarse voices of patients that had undergone partial cordectomisation. The simple structure of the proposed technique allows a real-time implementation, suitable for portable device realisation, as an aid to dysphonic speakers. It could be useful for reducing the effort in speaking, which is closely related to social problems due to awkwardness of voice.

  4. Localizing True Brain Interactions from EEG and MEG Data with Subspace Methods and Modified Beamformers

    Forooz Shahbazi Avarvand


    Full Text Available To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method “RAP-MUSIC” to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas.

  5. Localizing true brain interactions from EEG and MEG data with subspace methods and modified beamformers.

    Shahbazi Avarvand, Forooz; Ewald, Arne; Nolte, Guido


    To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method "RAP-MUSIC" to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a) RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b) conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c) the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas.

  6. Robust Recovery of Subspace Structures by Low-Rank Representation

    Liu, Guangcan; Yan, Shuicheng; Sun, Ju; Yu, Yong; Ma, Yi


    Data that arises from computer vision and image processing is often characterized by a mixture of multiple linear (or affine) subspaces, leading to the challenging problem of subspace segmentation. We observe that the heart of segmentation is to deal with the data that may not strictly follow subspace structures, i.e., to handle the data corrupted by noise. In this work we therefore address the subspace recovery problem. Given a set of data samples approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible noise as well, i.e., our goal is to recover the subspace structures from corrupted data. To this end, we propose low-rank representation (LRR) for recovering a low-rank data matrix from corrupted observations. The recovery is performed by seeking the lowest-rank representation among all the candidates that can represent the data vectors as linear combinations of the basis in a given dictionary. LRR fits well the subspac...

  7. Single-trial subspace-based approach for VEP extraction.

    Kamel, Nidal; Yusoff, Mohd Zuki; Hani, Ahmad Fadzil Mohamad


    A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram (EEG) colored noise without the need for a prewhitening stage is proposed. Linear estimation of the clean signal is performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data, and compared with the recently proposed signal subspace techniques. With the simulated data, the algorithms are used to estimate the latencies of P(100), P(200), and P(300) of VEP signals corrupted by additive colored noise at different values of SNR. With the real data, the VEP signals are collected at Selayang Hospital, Kuala Lumpur, Malaysia, and the capability of the proposed algorithm in detecting the latency of P(100) is obtained and compared with other subspace techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated significant improvement by the proposed technique in terms of better accuracy and less failure rate.

  8. Supervised orthogonal discriminant subspace projects learning for face recognition.

    Chen, Yu; Xu, Xiao-Hong


    In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP.

  9. Krylov Subspace Method with Communication Avoiding Technique for Linear System Obtained from Electromagnetic Analysis

    IKUNO, Soichiro; CHEN, Gong; YAMAMOTO, Susumu; ITOH, Taku; ABE, Kuniyoshi; NAKAMURA, Hiroaki


    Krylov subspace method and the variable preconditioned Krylov subspace method with communication avoiding technique for a linear system obtained from electromagnetic analysis are numerically investigated. In the k...

  10. Overview of Krylov subspace methods with applications to control problems

    Saad, Youcef


    An overview of projection methods based on Krylov subspaces are given with emphasis on their application to solving matrix equations that arise in control problems. The main idea of Krylov subspace methods is to generate a basis of the Krylov subspace Span and seek an approximate solution the the original problem from this subspace. Thus, the original matrix problem of size N is approximated by one of dimension m typically much smaller than N. Krylov subspace methods have been very successful in solving linear systems and eigenvalue problems and are now just becoming popular for solving nonlinear equations. It is shown how they can be used to solve partial pole placement problems, Sylvester's equation, and Lyapunov's equation.

  11. On iterative processes in the Krylov-Sonneveld subspaces

    Ilin, Valery P.


    The iterative Induced Dimension Reduction (IDR) methods are considered for solving large systems of linear algebraic equations (SLAEs) with nonsingular nonsymmetric matrices. These approaches are investigated by many authors and are charachterized sometimes as the alternative to the classical processes of Krylov type. The key moments of the IDR algorithms consist in the construction of the embedded Sonneveld subspaces, which have the decreasing dimensions and use the orthogonalization to some fixed subspace. Other independent approaches for research and optimization of the iterations are based on the augmented and modified Krylov subspaces by using the aggregation and deflation procedures with present various low rank approximations of the original matrices. The goal of this paper is to show, that IDR method in Sonneveld subspaces present an original interpretation of the modified algorithms in the Krylov subspaces. In particular, such description is given for the multi-preconditioned semi-conjugate direction methods which are actual for the parallel algebraic domain decomposition approaches.

  12. Classes of Invariant Subspaces for Some Operator Algebras

    Hamhalter, Jan; Turilova, Ekaterina


    New results showing connections between structural properties of von Neumann algebras and order theoretic properties of structures of invariant subspaces given by them are proved. We show that for any properly infinite von Neumann algebra M there is an affiliated subspace such that all important subspace classes living on are different. Moreover, we show that can be chosen such that the set of σ-additive measures on subspace classes of are empty. We generalize measure theoretic criterion on completeness of inner product spaces to affiliated subspaces corresponding to Type I factor with finite dimensional commutant. We summarize hitherto known results in this area, discuss their importance for mathematical foundations of quantum theory, and outline perspectives of further research.

  13. Subspace methods for pattern recognition in intelligent environment

    Jain, Lakhmi


    This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

  14. Robust video hashing via multilinear subspace projections.

    Li, Mu; Monga, Vishal


    The goal of video hashing is to design hash functions that summarize videos by short fingerprints or hashes. While traditional applications of video hashing lie in database searches and content authentication, the emergence of websites such as YouTube and DailyMotion poses a challenging problem of anti-piracy video search. That is, hashes or fingerprints of an original video (provided to YouTube by the content owner) must be matched against those uploaded to YouTube by users to identify instances of "illegal" or undesirable uploads. Because the uploaded videos invariably differ from the original in their digital representation (owing to incidental or malicious distortions), robust video hashes are desired. We model videos as order-3 tensors and use multilinear subspace projections, such as a reduced rank parallel factor analysis (PARAFAC) to construct video hashes. We observe that, unlike most standard descriptors of video content, tensor-based subspace projections can offer excellent robustness while effectively capturing the spatio-temporal essence of the video for discriminability. We introduce randomization in the hash function by dividing the video into (secret key based) pseudo-randomly selected overlapping sub-cubes to prevent against intentional guessing and forgery. Detection theoretic analysis of the proposed hash-based video identification is presented, where we derive analytical approximations for error probabilities. Remarkably, these theoretic error estimates closely mimic empirically observed error probability for our hash algorithm. Furthermore, experimental receiver operating characteristic (ROC) curves reveal that the proposed tensor-based video hash exhibits enhanced robustness against both spatial and temporal video distortions over state-of-the-art video hashing techniques.

  15. Ideals in the Roe Algebras of Discrete Metric Spaces with Coefficients in B(H)

    Yingjie HU; Qin WANG


    The notion of an ideal family of weighted subspaces of a discrete metric space X with bounded geometry is introduced.It is shown that,if X has Yu's property A,the ideal structure of the Roe algebra of X with coefficients in B(H) is completely characterized by the ideal families of weighted subspaces of X,where B(H) denotes the C*-algebra of bounded linear operators on a separable Hilbert space H.

  16. An alternative subspace approach to EEG dipole source localization

    Xu Xiaoliang [KC Science and Technologies Inc., Naperville, IL 60565 (United States); Xu, Bobby [Illinois Mathematics and Science Academy, Aurora, IL 60506 (United States); He Bin [Department of Bioengineering, University of Illinois, Chicago, IL 60607 (United States)


    In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.

  17. A New Inexact Inverse Subspace Iteration for Generalized Eigenvalue Problems

    Fatemeh Mohammad


    Full Text Available In this paper‎, ‎we represent an inexact inverse‎ ‎subspace iteration method for computing a few eigenpairs of the‎ ‎generalized eigenvalue problem $Ax = \\lambda Bx$[Q.~Ye and P.~Zhang‎, ‎Inexact inverse subspace iteration for generalized eigenvalue‎ ‎problems‎, ‎Linear Algebra and its Application‎, ‎434 (2011 1697-1715‎‎]‎. ‎In particular‎, ‎the linear convergence property of the inverse‎ ‎subspace iteration is preserved‎.

  18. Discrete Mathematics

    Sørensen, John Aasted


    The objectives of Discrete Mathematics (IDISM2) are: The introduction of the mathematics needed for analysis, design and verification of discrete systems, including the application within programming languages for computer systems. Having passed the IDISM2 course, the student will be able...... to accomplish the following: -Understand and apply formal representations in discrete mathematics. -Understand and apply formal representations in problems within discrete mathematics. -Understand methods for solving problems in discrete mathematics. -Apply methods for solving problems in discrete mathematics......; construct a finite state machine for a given application. Apply these concepts to new problems. The teaching in Discrete Mathematics is a combination of sessions with lectures and students solving problems, either manually or by using Matlab. Furthermore a selection of projects must be solved and handed...

  19. A Geometric Analysis of Subspace Clustering with Outliers

    Soltanolkotabi, Mahdi


    This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning applications, we do not know in advance how many subspaces there are nor do we have any information about their dimensions. We develop a novel geometric analysis of an algorithm named {\\em sparse subspace clustering} (SSC) \\cite{Elhamifar09}, which significantly broadens the range of problems where it is provably effective. For instance, we show that SSC can recover multiple subspaces, each of dimension comparable to the ambient dimension. We also prove that SSC can correctly cluster data points even when the subspaces of interest intersect. Further, we develop an extension of SSC that succeeds when the data set is corrupted with possibly overwhelmingly many outliers. Underlying our analysis are clear geometric insights, which may bear on other sparse recovery problems. A numerical study complements our theoretica...

  20. Review of Decoherence Free Subspaces, Noiseless Subsystems, and Dynamical Decoupling

    Lidar, Daniel A


    Quantum information requires protection from the adverse affects of decoherence and noise. This review provides an introduction to the theory of decoherence-free subspaces, noiseless subsystems, and dynamical decoupling. It addresses quantum information preservation as well protected computation.

  1. New results in subspace-stabilization control theory

    C. D. Johnson


    Full Text Available Subspace-stabilization is a generalization of the classical idea of stabilizing motions of a dynamical system to an equilibrium state. The concept of subspace-stabilization and a theory for designing subspace-stabilizing control laws was introduced in a previously published paper. In the present paper, two new alternative methods for designing control laws that achieve subspace-stabilization are presented. These two alternative design methods are based on: (i a novel application of existing Linear Quadratic Regulator optimal-control theory, and (ii an algebraic method in which the control-law is expressed as a linear feedback of certain “canonical variables.” In some cases, these new design methods may be more effective than existing ones. The results are illustrated by worked examples.

  2. Projections onto Invariant Subspaces of Some Banach Algebras



    In this paper,among other things,the author studies the weak*-closed left translation invariant complemented subspace of semigroup algebras and group algebras.Also,the author studiesthe relationships between projections and amenability.

  3. Modeling Sampling in Tensor Products of Unitary Invariant Subspaces

    Antonio G. García


    Full Text Available The use of unitary invariant subspaces of a Hilbert space H is nowadays a recognized fact in the treatment of sampling problems. Indeed, shift-invariant subspaces of L2(R and also periodic extensions of finite signals are remarkable examples where this occurs. As a consequence, the availability of an abstract unitary sampling theory becomes a useful tool to handle these problems. In this paper we derive a sampling theory for tensor products of unitary invariant subspaces. This allows merging the cases of finitely/infinitely generated unitary invariant subspaces formerly studied in the mathematical literature; it also allows introducing the several variables case. As the involved samples are identified as frame coefficients in suitable tensor product spaces, the relevant mathematical technique is that of frame theory, involving both finite/infinite dimensional cases.

  4. Unsupervised Spike Sorting Based on Discriminative Subspace Learning

    Keshtkaran, Mohammad Reza


    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separab...

  5. Manifold learning-based subspace distance for machinery damage assessment

    Sun, Chuang; Zhang, Zhousuo; He, Zhengjia; Shen, Zhongjie; Chen, Binqiang


    Damage assessment is very meaningful to keep safety and reliability of machinery components, and vibration analysis is an effective way to carry out the damage assessment. In this paper, a damage index is designed by performing manifold distance analysis on vibration signal. To calculate the index, vibration signal is collected firstly, and feature extraction is carried out to obtain statistical features that can capture signal characteristics comprehensively. Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace. The manifold learning algorithm seeks to keep local relationship of the feature matrix, which is more meaningful for damage assessment. Finally, Grassmann distance between manifold subspaces is defined as a damage index. The Grassmann distance reflecting manifold structure is a suitable metric to measure distance between subspaces in the manifold. The defined damage index is applied to damage assessment of a rotor and the bearing, and the result validates its effectiveness for damage assessment of machinery component.

  6. Wavelet Subspaces Invariant Under Groups of Translation Operators

    Biswaranjan Behera; Shobha Madan


    We study the action of translation operators on wavelet subspaces. This action gives rise to an equivalence relation on the set of all wavelets. We show by explicit construction that each of the associated equivalence classes is non-empty.


    Xu Changqing; Wang Hongyang; Song Wentao


    As the Projection Approximation Subspace Tracking with deflation(PASTd) algorithm is sensitive to impulsive noise, an improved subspace tracking algorithm is proposed and applied to blind adaptive multi-user detection. Simulation results show that the improved PASTd algorithm not only remains the properties of the conventional PASTdalgorithm, but also has good Bit Error Rate(BER) performance in impulsive noise environment, thus it can effectively improve the system performance.

  8. Evaluating Clustering in Subspace Projections of High Dimensional Data

    Müller, Emmanuel; Günnemann, Stephan; Assent, Ira


    Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation and co...... and create a common baseline for future developments and comparable evaluations in the field. For repeatability, all implementations, data sets and evaluation measures are available on our website....

  9. Experimental Comparison of Signal Subspace Based Noise Reduction Methods

    Hansen, Peter Søren Kirk; Hansen, Per Christian; Hansen, Steffen Duus


    The signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimato....... Experimental results show that the signal subspace approach may lead to a significant enhancement of the signal to noise ratio of the output signal....

  10. Experimental Comparison of Signal Subspace Based Noise Reduction Methods

    Hansen, Peter Søren Kirk; Hansen, Per Christian; Hansen, Steffen Duus; Sørensen, John Aasted


    The signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the signal subspace approach may lead to a significant enhancement of the signal to noise ratio of the output signal.

  11. On the structure of finitely generated shift-invariant subspaces

    Kazarian, K. S.


    A characterization of finitely generated shift-invariant subspaces is given when generators are g-minimal. An algorithm is given for the determination of the coefficients in the well known representation of the Fourier transform of an element of the finitely generated shift-invariant subspace as a linear combination of Fourier transformations of generators. An estimate for the norms of those coefficients is derived. For the proof a sort of orthogonalization procedure for generators is used wh...

  12. Krylov subspace acceleration of waveform relaxation

    Lumsdaine, A.; Wu, Deyun [Univ. of Notre Dame, IN (United States)


    Standard solution methods for numerically solving time-dependent problems typically begin by discretizing the problem on a uniform time grid and then sequentially solving for successive time points. The initial time discretization imposes a serialization to the solution process and limits parallel speedup to the speedup available from parallelizing the problem at any given time point. This bottleneck can be circumvented by the use of waveform methods in which multiple time-points of the different components of the solution are computed independently. With the waveform approach, a problem is first spatially decomposed and distributed among the processors of a parallel machine. Each processor then solves its own time-dependent subsystem over the entire interval of interest using previous iterates from other processors as inputs. Synchronization and communication between processors take place infrequently, and communication consists of large packets of information - discretized functions of time (i.e., waveforms).

  13. Discrete Curvature Theories and Applications

    Sun, Xiang


    Discrete Di erential Geometry (DDG) concerns discrete counterparts of notions and methods in di erential geometry. This thesis deals with a core subject in DDG, discrete curvature theories on various types of polyhedral surfaces that are practically important for free-form architecture, sunlight-redirecting shading systems, and face recognition. Modeled as polyhedral surfaces, the shapes of free-form structures may have to satisfy di erent geometric or physical constraints. We study a combination of geometry and physics { the discrete surfaces that can stand on their own, as well as having proper shapes for the manufacture. These proper shapes, known as circular and conical meshes, are closely related to discrete principal curvatures. We study curvature theories that make such surfaces possible. Shading systems of freeform building skins are new types of energy-saving structures that can re-direct the sunlight. From these systems, discrete line congruences across polyhedral surfaces can be abstracted. We develop a new curvature theory for polyhedral surfaces equipped with normal congruences { a particular type of congruences de ned by linear interpolation of vertex normals. The main results are a discussion of various de nitions of normality, a detailed study of the geometry of such congruences, and a concept of curvatures and shape operators associated with the faces of a triangle mesh. These curvatures are compatible with both normal congruences and the Steiner formula. In addition to architecture, we consider the role of discrete curvatures in face recognition. We use geometric measure theory to introduce the notion of asymptotic cones associated with a singular subspace of a Riemannian manifold, which is an extension of the classical notion of asymptotic directions. We get a simple expression of these cones for polyhedral surfaces, as well as convergence and approximation theorems. We use the asymptotic cones as facial descriptors and demonstrate the

  14. EVD Dualdating Based Online Subspace Learning

    Bo Jin


    Full Text Available Conventional incremental PCA methods usually only discuss the situation of adding samples. In this paper, we consider two different cases: deleting samples and simultaneously adding and deleting samples. To avoid the NP-hard problem of downdating SVD without right singular vectors and specific position information, we choose to use EVD instead of SVD, which is used by most IPCA methods. First, we propose an EVD updating and downdating algorithm, called EVD dualdating, which permits simultaneous arbitrary adding and deleting operation, via transforming the EVD of the covariance matrix into a SVD updating problem plus an EVD of a small autocorrelation matrix. A comprehensive analysis is delivered to express the essence, expansibility, and computation complexity of EVD dualdating. A mathematical theorem proves that if the whole data matrix satisfies the low-rank-plus-shift structure, EVD dualdating is an optimal rank-k estimator under the sequential environment. A selection method based on eigenvalues is presented to determine the optimal rank k of the subspace. Then, we propose three incremental/decremental PCA methods: EVDD-IPCA, EVDD-DPCA, and EVDD-IDPCA, which are adaptive to the varying mean. Finally, plenty of comparative experiments demonstrate that EVDD-based methods outperform conventional incremental/decremental PCA methods in both efficiency and accuracy.

  15. Subspace Expanders and Matrix Rank Minimization

    Khajehnejad, Amin; Hassibi, Babak


    Matrix rank minimization (RM) problems recently gained extensive attention due to numerous applications in machine learning, system identification and graphical models. In RM problem, one aims to find the matrix with the lowest rank that satisfies a set of linear constraints. The existing algorithms include nuclear norm minimization (NNM) and singular value thresholding. Thus far, most of the attention has been on i.i.d. Gaussian measurement operators. In this work, we introduce a new class of measurement operators, and a novel recovery algorithm, which is notably faster than NNM. The proposed operators are based on what we refer to as subspace expanders, which are inspired by the well known expander graphs based measurement matrices in compressed sensing. We show that given an $n\\times n$ PSD matrix of rank $r$, it can be uniquely recovered from a minimal sampling of $O(nr)$ measurements using the proposed structures, and the recovery algorithm can be cast as matrix inversion after a few initial processing s...


    ZHANG Zhiyi; FAN Jiangling; HUA Hongxing


    An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix. In combination with component energy index (CEI) which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio (SNR)and gives a reliable separation of spurious and physical estimates.

  17. Noise Tracking Using DFT Domain Subspace Decompositions

    Hendriks, R.C.; Jensen, J.; Heusdens, R.


    All discrete Fourier transform (DFT) domain-based speech enhancement gain functions rely on knowledge of the noise power spectral density (PSD). Since the noise PSD is unknown in advance, estimation from the noisy speech signal is necessary. An overestimation of the noise PSD will lead to a loss in

  18. Noise Tracking Using DFT Domain Subspace Decompositions

    Hendriks, R.C.; Jensen, J.; Heusdens, R.


    All discrete Fourier transform (DFT) domain-based speech enhancement gain functions rely on knowledge of the noise power spectral density (PSD). Since the noise PSD is unknown in advance, estimation from the noisy speech signal is necessary. An overestimation of the noise PSD will lead to a loss in

  19. Noncommutative Fourier Analysis on Invariant Subspaces: Frames of Unitary Orbits and Hilbert Modules over Group von Neumann Algebras

    Davide Barbieri


    Full Text Available This is a joint work with E. Hernández, J. Parcet and V. Paternostro. We will discuss the structure of bases and frames of unitary orbits of discrete groups in invariant subspaces of separable Hilbert spaces. These invariant spaces can be characterized, by means of Fourier intertwining operators, as modules whose rings of coefficients are given by the group von Neumann algebra, endowed with an unbounded operator valued pairing which defines a noncommutative Hilbert structure. Frames and bases obtained by countable families of orbits have noncommutative counterparts in these Hilbert modules, given by countable families of operators satisfying generalized reproducing conditions. These results extend key notions of Fourier and wavelet analysis to general unitary actions of discrete groups, such as crystallographic transformations on the Euclidean plane or discrete Heisenberg groups.

  20. Discrete Breathers

    Flach, S


    Nonlinear classical Hamiltonian lattices exhibit generic solutions in the form of discrete breathers. These solutions are time-periodic and (typically exponentially) localized in space. The lattices exhibit discrete translational symmetry. Discrete breathers are not confined to certain lattice dimensions. Necessary ingredients for their occurence are the existence of upper bounds on the phonon spectrum (of small fluctuations around the groundstate) of the system as well as the nonlinearity in the differential equations. We will present existence proofs, formulate necessary existence conditions, and discuss structural stability of discrete breathers. The following results will be also discussed: the creation of breathers through tangent bifurcation of band edge plane waves; dynamical stability; details of the spatial decay; numerical methods of obtaining breathers; interaction of breathers with phonons and electrons; movability; influence of the lattice dimension on discrete breather properties; quantum lattic...

  1. Method of change of the subspace of control parameters and its application to problems of synthesis of nonlinearly deformable axisymmetric thin-walled structures

    Gavryushin, S. S.; Nikolaeva, A. S.


    The theoretical foundations, methods, and algorithms developed to analyze the stability and postbuckling behavior of thin elastic axisymmetric shells are discussed. The algorithm for numerically studying the processes of nonlinear deformation of thin-walled axisymmetric shells by the solution parametric continuation method is generalized to solving the practical problem of design of mechanical actuators of discrete action. The synthesis algorithm is based on the method of changing the subspace of control parameters, which is supplemented with the procedure of smooth transition in changing the subspaces. The efficiency of the proposed algorithm is illustrated by an example of synthesis of a thermobimetallic actuator of discrete action. The procedure of determining an isolated solution, whose existencewas predicted byV. I. Feodosiev, is considered in the framework of studying the process of nonlinear deformation of a corrugated membrane loaded by an external pressure.

  2. Choosing parameters of kernel subspace LDA for recognition of face images under pose and illumination variations.

    Huang, Jian; Yuen, Pong C; Chen, Wen-Sheng; Lai, Jian Huang


    This paper addresses the problem of automatically tuning multiple kernel parameters for the kernel-based linear discriminant analysis (LDA) method. The kernel approach has been proposed to solve face recognition problems under complex distribution by mapping the input space to a high-dimensional feature space. Some recognition algorithms such as the kernel principal components analysis, kernel Fisher discriminant, generalized discriminant analysis, and kernel direct LDA have been developed in the last five years. The experimental results show that the kernel-based method is a good and feasible approach to tackle the pose and illumination variations. One of the crucial factors in the kernel approach is the selection of kernel parameters, which highly affects the generalization capability and stability of the kernel-based learning methods. In view of this, we propose an eigenvalue-stability-bounded margin maximization (ESBMM) algorithm to automatically tune the multiple parameters of the Gaussian radial basis function kernel for the kernel subspace LDA (KSLDA) method, which is developed based on our previously developed subspace LDA method. The ESBMM algorithm improves the generalization capability of the kernel-based LDA method by maximizing the margin maximization criterion while maintaining the eigenvalue stability of the kernel-based LDA method. An in-depth investigation on the generalization performance on pose and illumination dimensions is performed using the YaleB and CMU PIE databases. The FERET database is also used for benchmark evaluation. Compared with the existing PCA-based and LDA-based methods, our proposed KSLDA method, with the ESBMM kernel parameter estimation algorithm, gives superior performance.

  3. Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis.

    Fang, Shu; Li, Jia; Tian, Yonghong; Huang, Tiejun; Chen, Xiaowu


    In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets and suppressing distractors. Toward this end, we first conduct principal component analysis on massive randomly selected image patches. The principal components, which correspond to the largest eigenvalues, are selected to construct candidate subspaces since they often demonstrate impressive abilities to separate targets and distractors. By projecting images onto various subspaces, we further characterize each image patch by its contrasts against randomly selected neighboring and peripheral regions. In this manner, the probable targets often have the highest responses, while the responses at background regions become very low. Based on such random contrasts, an optimization framework with pairwise binary terms is adopted to learn the saliency model that best separates salient targets and distractors by optimally integrating the cues from various subspaces. Experimental results on two public benchmarks show that the proposed approach outperforms 16 state-of-the-art methods in human fixation prediction.

  4. Discrete Stein characterizations and discrete information distances

    Ley, Christophe


    We construct two different Stein characterizations of discrete distributions and use these to provide a natural connection between Stein characterizations for discrete distributions and discrete information functionals.

  5. Semi-Classical field theory as Decoherence Free Subspaces

    Varela, Jaime


    We formulate semi-classical field theory as an approximate decoherence-free-subspace of a finite-dimensional quantum-gravity hilbert space. A complementarity construction can be realized as a unitary transformation which changes the decoherence-free-subspace. This can be translated to signify that field theory on a global slice, in certain space-times, is the simultaneous examination of two different superselected sectors of a gauge theory. We posit that a correct course graining procedure of quantum gravity should be WKB states propagating in a curved background in which particles exiting a horizon have imaginary components to their phases. The field theory appears non-unitary, but it is due to the existence of approximate decoherence free sub-spaces. Furthermore, the importance of operator spaces in the course-graining procedure is discussed. We also briefly touch on Firewalls.

  6. Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization

    Fornasier, Massimo


    This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a seminorm for a subspace. The optimization is realized by alternating minimizations of the functional on a sequence of orthogonal subspaces. On each subspace an iterative proximity-map algorithm is implemented via oblique thresholding, which is the main new tool introduced in this work. We provide convergence conditions for the algorithm in order to compute minimizers of the target energy. Analogous results are derived for a parallel variant of the algorithm. Applications are presented in domain decomposition methods for degenerate elliptic PDEs arising in total variation minimization and in accelerated sparse recovery algorithms based on 1-minimization. We include numerical examples which show e.cient solutions to classical problems in signal and image processing. © 2009 Society for Industrial and Applied Physics.

  7. Poset pinball, GKM-compatible subspaces, and Hessenberg varieties

    Harada, Megumi


    This paper has three main goals. First, we set up a general framework to address the problem of constructing module bases for the equivariant cohomology of certain subspaces of GKM spaces. To this end we introduce the notion of a GKM-compatible subspace of an ambient GKM space. We also discuss poset-upper-triangularity, a key combinatorial notion in both GKM theory and more generally in localization theory in equivariant cohomology. With a view toward other applications, we present parts of our setup in a general algebraic and combinatorial framework. Second, motivated by our central problem of building module bases, we introduce a combinatorial game which we dub poset pinball and illustrate with several examples. Finally, as first applications, we apply the perspective of GKM-compatible subspaces and poset pinball to construct explicit and computationally convenient module bases for the $S^1$-equivariant cohomology of all Peterson varieties of classical Lie type, and subregular Springer varieties of Lie type...

  8. Subspace Properties of Network Coding and their Applications

    Siavoshani, Mahdi Jafari; Diggavi, Suhas


    Systems that employ network coding for content distribution convey to the receivers linear combinations of the source packets. If we assume randomized network coding, during this process the network nodes collect random subspaces of the space spanned by the source packets. We establish several fundamental properties of the random subspaces induced in such a system, and show that these subspaces implicitly carry topological information about the network and its state that can be passively collected and inferred. We leverage this information towards a number of applications that are interesting in their own right, such as topology inference, bottleneck discovery in peer-to-peer systems and locating Byzantine attackers. We thus argue that, randomized network coding, apart from its better known properties for improving information delivery rate, can additionally facilitate network management and control.

  9. Reduced-Rank Adaptive Filtering Using Krylov Subspace

    Sergueï Burykh


    Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.


    Mohammad Sadegh Asgari


    A frame is an orthonormal basis-like collection of vectors in a Hilbert space,but need not be a basis or orthonormal.A fusion frame (frame of subspaces) is a frame-like collection of subspaces in a Hilbert space,thereby constructing a frame for the whole space by joining sequences of frames for subspaces.Moreover the notion of fusion frames provide a framework for applications and providing efficient and robust information processing algorithms.In this paper we study the conditions under which removing an element from a fusion frame,again we obtain another fusion frame.We give another proof of[5,Corollary 3.3(iii)]with extra information about the bounds.

  11. Discrete Mathematics

    Sørensen, John Aasted


    The introduction of the mathematics needed for analysis, design and verification of discrete systems, including applications within programming languages for computer systems. Course sessions and project work. Semester: Spring 2010 Ectent: 5 ects Class size: 18......The introduction of the mathematics needed for analysis, design and verification of discrete systems, including applications within programming languages for computer systems. Course sessions and project work. Semester: Spring 2010 Ectent: 5 ects Class size: 18...

  12. Discrete Mathematics

    Sørensen, John Aasted


    The introduction of the mathematics needed for analysis, design and verification of discrete systems, including applications within programming languages for computer systems. Course sessions and project work. Semester: Autumn 2010 Ectent: 5 ects Class size: 15......The introduction of the mathematics needed for analysis, design and verification of discrete systems, including applications within programming languages for computer systems. Course sessions and project work. Semester: Autumn 2010 Ectent: 5 ects Class size: 15...

  13. Krylov-subspace acceleration of time periodic waveform relaxation

    Lumsdaine, A. [Univ. of Notre Dame, IN (United States)


    In this paper the author uses Krylov-subspace techniques to accelerate the convergence of waveform relaxation applied to solving systems of first order time periodic ordinary differential equations. He considers the problem in the frequency domain and presents frequency dependent waveform GMRES (FDWGMRES), a member of a new class of frequency dependent Krylov-subspace techniques. FDWGMRES exhibits many desirable properties, including finite termination independent of the number of timesteps and, for certain problems, a convergence rate which is bounded from above by the convergence rate of GMRES applied to the static matrix problem corresponding to the linear time-invariant ODE.




    In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual disbution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.

  15. Roller Bearing Monitoring by New Subspace-Based Damage Indicator

    G. Gautier


    Full Text Available A frequency-band subspace-based damage identification method for fault diagnosis in roller bearings is presented. Subspace-based damage indicators are obtained by filtering the vibration data in the frequency range where damage is likely to occur, that is, around the bearing characteristic frequencies. The proposed method is validated by considering simulated data of a damaged bearing. Also, an experimental case is considered which focuses on collecting the vibration data issued from a run-to-failure test. It is shown that the proposed method can detect bearing defects and, as such, it appears to be an efficient tool for diagnosis purpose.

  16. A subspace preconditioning algorithm for eigenvector/eigenvalue computation

    Bramble, J.H.; Knyazev, A.V.; Pasciak, J.E.


    We consider the problem of computing a modest number of the smallest eigenvalues along with orthogonal bases for the corresponding eigen-spaces of a symmetric positive definite matrix. In our applications, the dimension of a matrix is large and the cost of its inverting is prohibitive. In this paper, we shall develop an effective parallelizable technique for computing these eigenvalues and eigenvectors utilizing subspace iteration and preconditioning. Estimates will be provided which show that the preconditioned method converges linearly and uniformly in the matrix dimension when used with a uniform preconditioner under the assumption that the approximating subspace is close enough to the span of desired eigenvectors.

  17. New Estimates for the Rate of Convergence of the Method of Subspace Corrections

    Durkbin Cho; Jinchao Xu; Ludmil Zikatanov


    We discuss estimates for the rate of convergence of the method of successive subspace corrections in terms of condition number estimate for the method of parallel subspace corrections. We provide upper bounds and in a special case, a lower bound for preconditioners defined via the method of successive subspace corrections.

  18. General Scheme for the Construction of a Protected Qubit Subspace

    Aharon, N.; Drewsen, M.; Retzker, A.


    We present a new robust decoupling scheme suitable for half integer angular momentum states. The scheme is based on continuous dynamical decoupling techniques by which we create a protected qubit subspace. Our scheme predicts a coherence time of ~1 second, as compared to typically a few...

  19. Robust Visual Tracking via Sparsity-Induced Subspace Learning.

    Sui, Yao; Zhang, Shunli; Zhang, Li


    Target representation is a necessary component for a robust tracker. However, during tracking, many complicated factors may make the accumulated errors in the representation significantly large, leading to tracking drift. This paper aims to improve the robustness of target representation to avoid the influence of the accumulated errors, such that the tracker only acquires the information that facilitates tracking and ignores the distractions. We observe that the locally mutual relations between the feature observations of temporally obtained targets are beneficial to the subspace representation in visual tracking. Thus, we propose a novel subspace learning algorithm for visual tracking, which imposes joint row-wise sparsity structure on the target subspace to adaptively exclude distractive information. The sparsity is induced by exploiting the locally mutual relations between the feature observations during learning. To this end, we formulate tracking as a subspace sparsity inducing problem. A large number of experiments on various challenging video sequences demonstrate that our tracker outperforms many other state-of-the-art trackers.

  20. New Characterizations of Fusion Frames (Frames of Subspaces)

    Mohammad Sadegh Asgari


    In this article, we give new characterizations of fusion frames, on the properties of their synthesis operators, on the behavior of fusion frames under bounded operators with closed range, and on erasures of subspaces of fusion frames. Furthermore we show that every fusion frame is the image of an orthonormal fusion basis under a bounded surjective operator.

  1. Decoherence free subspaces of a quantum Markov semigroup

    Agredo, Julián, E-mail: [Centro de Análisis Estocástico, Facultad de Ingeniería, Universidad Católica de Chile, Santiago, Chile and Departamento de Matemáticas, Universidad Nacional de Colombia, Manizales (Colombia); Fagnola, Franco, E-mail: [Dipartimento di Matematica, Politecnico di Milano, Milano (Italy); Rebolledo, Rolando, E-mail: [Centro de Análisis Estocástico, Facultad de Ingeniería, Facultad de Matemáticas, Universidad Católica de Chile, Santiago (Chile)


    We give a full characterisation of decoherence free subspaces of a given quantum Markov semigroup with generator in a generalised Lindbald form which is valid also for infinite-dimensional systems. Our results, extending those available in the literature concerning finite-dimensional systems, are illustrated by some examples.

  2. Amendment on DPM and OJA Class Subspace Tracking Methods

    Cheng, Zhu; Liu, Haitao; Ahmadi, Majid


    After analysis of the updating formula of DPM and OJA class of subspace tracking method, the reason for the spark in the stead state projector error power is discovered. The spark problem was fixed by the application of a limiter on update stepsize. The simulation confirmed the elimination of the overtune error.

  3. Contractively complemented subspaces of pre-symmetric spaces

    Neal, Matthew


    In 1965, Ron Douglas proved that if $X$ is a closed subspace of an $L^1$-space and $X$ is isometric to another $L^1$-space, then $X$ is the range of a contractive projection on the containing $L^1$-space. In 1977 Arazy-Friedman showed that if a subspace $X$ of $C_1$ is isometric to another $C_1$-space (possibly finite dimensional), then there is a contractive projection of $C_1$ onto $X$. In 1993 Kirchberg proved that if a subspace $X$ of the predual of a von Neumann algebra $M$ is isometric to the predual of another von Neumann algebra, then there is a contractive projection of the predual of $M$ onto $X$. We widen significantly the scope of these results by showing that if a subspace $X$ of the predual of a $JBW^*$-triple $A$ is isometric to the predual of another $JBW^*$-triple $B$, then there is a contractive projection on the predual of $A$ with range $X$, as long as $B$ does not have a direct summand which is isometric to a space of the form $L^\\infty(\\Omega,H)$, where $H$ is a Hilbert space of dimensio...

  4. Problem of invariant subspaces in C*-algebras



    Let A be a separable simple C*-algebra. For each a(≠0) in A, there exists a separable faithful and irreducible * representation (π, Hπ) on A such that π(a) has a non-trivial invariant subspace in Hπ.

  5. Subspace Iteration Algorithms in FORTRAN 77 and FORTRAN 8x


    AFWAL-TR-88-3120 SUBSPACE ITERATION ?ALGORITHMS IN N FORTRAN 77 AND Cy FORTRAN 8x Q0 Paul J. Nikolai AFWAL/ FIBRA AUG a 289 December 1988 U Final...employed by your organization please notify AFWAI FIBRA . WPAFB, OH 45433-6553 to help us maintain a current mailing list . Copies of this report

  6. Active Subspace Methods for Data-Intensive Inverse Problems

    Wang, Qiqi [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)


    The project has developed theory and computational tools to exploit active subspaces to reduce the dimension in statistical calibration problems. This dimension reduction enables MCMC methods to calibrate otherwise intractable models. The same theoretical and computational tools can also reduce the measurement dimension for calibration problems that use large stores of data.

  7. Discrete Chaos

    Waelbroeck, H


    We propose a theory of deterministic chaos for discrete systems, based on their representations in symbolic history spaces Ømega. These are spaces of semi-infinite sequences, as the one-sided shift spaces, but endowed with a more general topology which we call a semicausal topology. We show that define metrical properties, including the correlation dimension of the attractor. Examples are considered: Asymmetric neural networks and random cellular automata are not chaotic. A neural network model with memory, on the other hand, does appear to be an example of discrete chaos.

  8. Discrete mechanics

    Caltagirone, Jean-Paul


    This book presents the fundamental principles of mechanics to re-establish the equations of Discrete Mechanics. It introduces physics and thermodynamics associated to the physical modeling.  The development and the complementarity of sciences lead to review today the old concepts that were the basis for the development of continuum mechanics. The differential geometry is used to review the conservation laws of mechanics. For instance, this formalism requires a different location of vector and scalar quantities in space. The equations of Discrete Mechanics form a system of equations where the H

  9. Territorial discretion

    Augusto Hernández Vidal


    Full Text Available In order to strengthen the concept of municipal autonomy, this essay proposes an extensive interpretation of administrative discretion. Discretion is the exercise of free judgment given by law to authorities for performing official acts. This legislative technique seems to be suitable whenever the legislative is intended to legislate over the essential core of municipal autonomy. This way, an eventual abuse of that autonomy could be avoided, for the disproportional restriction of the local faculty to oversee the local issues. This alternative is presented as a tool to provide with dynamism the performing of administrative activities as well, aiming to assimilate public administration new practices.

  10. Online Categorical Subspace Learning for Sketching Big Data with Misses

    Shen, Yanning; Mardani, Morteza; Giannakis, Georgios B.


    With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical samples, the typically streaming format of acquisition, and the possibly missing entries. To cope with these challenges, the present paper develops a novel categorical subspace learning approach to unravel the latent structure for three prominent categorical (bilinear) models, namely, Probit, Tobit, and Logit. The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios. Leveraging the low intrinsic dimensionality of the sought models, a rank regularized maximum-likelihood estimator is devised, which is then solved recursively via alternating majorization-minimization to sketch high-dimensional categorical data `on the fly.' The resultant procedure alternates between sketching the new incomplete datum and refining the latent subspace, leading to lightweight first-order algorithms with highly parallelizable tasks per iteration. As an extra degree of freedom, the quantization thresholds are also learned jointly along with the subspace to enhance the predictive power of the sought models. Performance of the subspace iterates is analyzed for both infinite and finite data streams, where for the former asymptotic convergence to the stationary point set of the batch estimator is established, while for the latter sublinear regret bounds are derived for the empirical cost. Simulated tests with both synthetic and real-world datasets corroborate the merits of the novel schemes for real-time movie recommendation and chess-game classification.

  11. Subspaces indexing model on Grassmann manifold for image search.

    Wang, Xinchao; Li, Zhu; Tao, Dacheng


    Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribution we are trying to model is typically nonlinear. Moreover, these algorithms fail to incorporate local variations of the intrinsic sample distribution manifold. Therefore, these algorithms are ineffective when applied on large scale datasets. Kernel versions of these approaches can alleviate the problem to certain degree but face a serious computational challenge when data set is large, where the computing involves Eigen/QP problems of size N × N. When N is large, kernel versions are not computationally practical. To tackle the aforementioned problems and improve recognition/searching performance, especially on large scale image datasets, we propose a novel local subspace indexing model for image search termed Subspace Indexing Model on Grassmann Manifold (SIM-GM). SIM-GM partitions the global space into local patches with a hierarchical structure; the global model is, therefore, approximated by piece-wise linear local subspace models. By further applying the Grassmann manifold distance, SIM-GM is able to organize localized models into a hierarchy of indexed structure, and allow fast query selection of the optimal ones for classification. Our proposed SIM-GM enjoys a number of merits: 1) it is able to deal with a large number of training samples efficiently; 2) it is a query-driven approach, i.e., it is able to return an effective local space model, so the recognition performance could be significantly improved; 3) it is a common framework, which can incorporate many learning algorithms. Theoretical analysis and extensive experimental results confirm the validity of this model.

  12. A signal subspace dimension estimator based on F-norm with application to subspace-based multi-channel speech enhancement

    LI Chao; LIU Wenju


    Although the signal subspace approach has been studied extensively for speech enhancement, no good solution has been found to identify signal subspace dimension in multi- channel situation. This paper presents a signal subspace dimension estimator based on F-norm of correlation matrix, with which subspace-based multi-channel speech enhancement is robust to adverse acoustic environments such as room reverberation and low input signal to noise ratio (SNR). Experiments demonstrate the presented method leads to more noise reduction and less speech distortion comparing with traditional methods.

  13. Profit maximization mitigates competition

    Dierker, Egbert; Grodal, Birgit


    We consider oligopolistic markets in which the notion of shareholders' utility is well-defined and compare the Bertrand-Nash equilibria in case of utility maximization with those under the usual profit maximization hypothesis. Our main result states that profit maximization leads to less price...... competition than utility maximization. Since profit maximization tends to raise prices, it may be regarded as beneficial for the owners as a whole. Moreover, if profit maximization is a good proxy for utility maximization, then there is no need for a general equilibrium analysis that takes the distribution...... of profits among consumers fully into account and partial equilibrium analysis suffices...

  14. Improved electromagnetic induction processing with novel adaptive matched filter and matched subspace detection

    Hayes, Charles E.; McClellan, James H.; Scott, Waymond R.; Kerr, Andrew J.


    This work introduces two advances in wide-band electromagnetic induction (EMI) processing: a novel adaptive matched filter (AMF) and matched subspace detection methods. Both advances make use of recent work with a subspace SVD approach to separating the signal, soil, and noise subspaces of the frequency measurements The proposed AMF provides a direct approach to removing the EMI self-response while improving the signal to noise ratio of the data. Unlike previous EMI adaptive downtrack filters, this new filter will not erroneously optimize the EMI soil response instead of the EMI target response because these two responses are projected into separate frequency subspaces. The EMI detection methods in this work elaborate on how the signal and noise subspaces in the frequency measurements are ideal for creating the matched subspace detection (MSD) and constant false alarm rate matched subspace detection (CFAR) metrics developed by Scharf The CFAR detection metric has been shown to be the uniformly most powerful invariant detector.

  15. Discrete Mathematics

    Sørensen, John Aasted


    examples on regular languages. Apply these concepts to new problems. Finite state machines: Define a finite state machine as a 6-tuble; describe simple finite state machines by tables and graphs; pattern recognition by finite state machines; minimizing the number of states in a finite state machine......The objectives of Discrete Mathematics (IDISM2) are: The introduction of the mathematics needed for analysis, design and verification of discrete systems, including the application within programming languages for computer systems. Having passed the IDISM2 course, the student will be able...... of natural numbers. Apply these concepts to new problems. Division and factorizing: Define a prime number and apply Euclid´s algorithm for factorizing an integer. Regular languages: Define a language from the elements of a set; define a regular language; form strings from a regular language; construct...

  16. Manipulating quantum information on the controllable systems or subspaces

    Zhang, Ming


    In this paper, we explore how to constructively manipulate quantum information on the controllable systems or subspaces. It is revealed that one can make full use of distinguished properties of Pauli operators to design control Hamiltonian based on the geometric parametrization of quantum states. It is demonstrated in this research that Bang-Bang controls, triangle-function controls and square-function control can be utilized to manipulate controllable qubits or encoded qubits on controllable subspace for both open quantum dynamical systems and uncontrollable closed quantum dynamical systems. Furthermore, we propose a new kind of time-energy performance index to trade-off time and energy resource cost, and comprehensively discuss how to design control magnitude to minimize a kind of time-energy performance. A comparison has been made among these three kind of optimal control. It is underlined in this research that the optimal time performance can be always expressed as J^{*} =\\lamda{\\cdot}t^{*}_{f} +E^{*} for...

  17. Evaluating Clustering in Subspace Projections of High Dimensional Data

    Müller, Emmanuel; Günnemann, Stephan; Assent, Ira


    of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention. In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering......Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation...... and comparison between these paradigms on a common basis. Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects...

  18. A Target Recognition Approach to Projecting HRR Profiles onto Subspace

    裴炳南; 保铮


    A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixes are constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Taking the methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Whereupon, a template library for target recognition is built by the projection of a class-mean vector made from the radar data onto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and each template in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar data arte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noise when SNR≥5dB.

  19. Unified Model in Identity Subspace for Face Recognition

    Pin Liao; Li Shen; Yi-Qiang Chen; Shu-Chang Liu


    Human faces have two important characteristics: (1) They are similar objects and the specific variations of each face are similar to each other; (2) They are nearly bilateral symmetric. Exploiting the two important properties, we build a unified model in identity subspace (UMIS) as a novel technique for face recognition from only one example image per person. An identity subspace spanned by bilateral symmetric bases, which compactly encodes identity information, is presented. The unified model, trained on an obtained training set with multiple samples per class from a known people group A, can be generalized well to facial images of unknown individuals,and can be used to recognize facial images from an unknown people group B with only one sample per subject.Extensive experimental results on two public databases (the Yale database and the Bern database) and our own database (the ICT-JDL database) demonstrate that the UMIS approach is significantly effective and robust for face recognition.

  20. View subspaces for indexing and retrieval of 3D models

    Dutagaci, Helin; Sankur, Bulent; Yemez, Yücel


    View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We a...

  1. User Scheduling for Heterogeneous Multiuser MIMO Systems: A Subspace Viewpoint

    Yi, Xinping


    In downlink multiuser multiple-input multiple-output (MU-MIMO) systems, users are practically heterogeneous in nature. However, most of the existing user scheduling algorithms are designed with an implicit assumption that the users are homogeneous. In this paper, we revisit the problem by exploring the characteristics of heterogeneous users from a subspace point of view. With an objective of minimizing interference non-orthogonality among users, three new angular-based user scheduling criteria that can be applied in various user scheduling algorithms are proposed. While the first criterion is heuristically determined by identifying the incapability of largest principal angle to characterize the subspace correlation and hence the interference non-orthogonality between users, the second and third ones are derived by using, respectively, the sum rate capacity bounds with block diagonalization and the change in capacity by adding a new user into an existing user subset. Aiming at capturing fairness among heteroge...

  2. Integrated Phoneme Subspace Method for Speech Feature Extraction

    Park Hyunsin


    Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

  3. Eigenvoice-based MAP adaptation within correlation subspace

    LUO Jun; OU Zhi-jian; WANG Zuo-ying


    In recent years,the eigenvoice approach has proven to be an efficient method for rapid speaker adaptation,which directs the adaptation according to the analysis of full speaker vector space.In this article,we developed a new algorithm for eigenspace-based adaptation restricting eigenvoices in clustered subspaces, and maximum likelihood (ML) criterion was replaced with maximum aposteriori (MAP) criterion for better parameter estimation.Experiments show that even with one sentence adaptation data this algorithm would result in 6.45 % error ratio reduction relatively,which overcomes the instability of maximum likelihood linear regression (MLLR) with limited data and is much faster than traditional MAP method.This algorithm is not highly-dependent on subspace number of division,thus it proved to be a robust adaptation algorithm.

  4. Mining visual collocation patterns via self-supervised subspace learning.

    Yuan, Junsong; Wu, Ying


    Traditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.

  5. Noise Robust Joint Sparse Recovery using Compressive Subspace Fitting

    Kim, Jong Min; Ye, Jong Chul


    We study a multiple measurement vector (MMV) problem where multiple signals share a common sparse support set and are sampled by a common sensing matrix. Although we can expect that joint sparsity can improve the recovery performance over a single measurement vector (SMV) problem, compressive sensing (CS) algorithms for MMV exhibit performance saturation as the number of multiple signals increases. Recently, to overcome these drawbacks of CS approaches, hybrid algorithms that optimally combine CS with sensor array signal processing using a generalized MUSIC criterion have been proposed. While these hybrid algorithms are optimal for critically sampled cases, they are not efficient in exploiting the redundant sampling to improve noise robustness. Hence, in this work, we introduce a novel subspace fitting criterion that extends the generalized MUSIC criterion so that it exhibits near-optimal behaviors for various sampling conditions. In addition, the subspace fitting criterion leads to two alternative forms of c...

  6. Face recognition based on LDA in manifold subspace

    Hung Phuoc Truong


    Full Text Available Although LDA has many successes in dimensionality reduction and data separation, it also has disadvantages, especially the small sample size problem in training data because the "within-class scatter" matrix may not be accurately estimated. Moreover, this algorithm can only operate correctly with labeled data in supervised learning. In practice, data collection is very huge and labeling data requires high-cost, thus the combination of a part of labeled data and unlabeled data for this algorithm in Manifold subspace is a novelty research. This paper reports a study that propose a semi-supervised method called DSLM, which aims at overcoming all these limitations. The proposed method ensures that the discriminative information of labeled data and the intrinsic geometric structure of data are mapped to new optimal subspace. Results are obtained from the experiments and compared to several related methods showing the effectiveness of our proposed method.

  7. The Lie Algebras in which Every Subspace s Its Subalgebra



    In this paper, we study the Lie algebras in which every subspace is its subalgebra (denoted by HB Lie algebras). We get that a nonabelian Lie algebra is an HB Lie algebra if and only if it is isomorphic to g+Cidg, where g is an abelian Lie algebra. Moreover we show that the derivation algebra and the holomorph of a nonabelian HB Lie algebra are complete.


    Zi-Luan Wei


    A subspace search method for solving a class of least squares problem is pre sented in the paper. The original problem is divided into many independent sub problems, and a search direction is obtained by solving each of the subproblems, as well as a new iterative point is determined by choosing a suitable steplength such that the value of residual norm is decreasing. The convergence result is also given. The numerical test is also shown for a special problem,

  9. Index Formulae for Subspaces of Kreĭn Spaces

    Dijksma, Aad; Gheondea, Aurelian


    For a subspace S of a Kreĭn space K and an arbitrary fundamental decomposition K = K-[+]K+ of K, we prove the index formula κ-(S) + dim(S⊥ ∩ K+) = κ+(S⊥) + dim(S ∩ K-), where κ±(S) stands for the positive/negative signature of S. The difference dim(S ∩ K-) - dim(S⊥ ∩ K+), provided it is well defined

  10. An adaptation of Krylov subspace methods to path following

    Walker, H.F. [Utah State Univ., Logan, UT (United States)


    Krylov subspace methods at present constitute a very well known and highly developed class of iterative linear algebra methods. These have been effectively applied to nonlinear system solving through Newton-Krylov methods, in which Krylov subspace methods are used to solve the linear systems that characterize steps of Newton`s method (the Newton equations). Here, we will discuss the application of Krylov subspace methods to path following problems, in which the object is to track a solution curve as a parameter varies. Path following methods are typically of predictor-corrector form, in which a point near the solution curve is {open_quotes}predicted{close_quotes} by some easy but relatively inaccurate means, and then a series of Newton-like corrector iterations is used to return approximately to the curve. The analogue of the Newton equation is underdetermined, and an additional linear condition must be specified to determine corrector steps uniquely. This is typically done by requiring that the steps be orthogonal to an approximate tangent direction. Augmenting the under-determined system with this orthogonality condition in a straightforward way typically works well if direct linear algebra methods are used, but Krylov subspace methods are often ineffective with this approach. We will discuss recent work in which this orthogonality condition is imposed directly as a constraint on the corrector steps in a certain way. The means of doing this preserves problem conditioning, allows the use of preconditioners constructed for the fixed-parameter case, and has certain other advantages. Experiments on standard PDE continuation test problems indicate that this approach is effective.

  11. Improved Stochastic Subspace System Identification for Structural Health Monitoring

    Chang, Chia-Ming; Loh, Chin-Hsiung


    Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.

  12. Classification of Polarimetric SAR Image Based on the Subspace Method

    Xu, J.; Li, Z.; Tian, B.; Chen, Q.; Zhang, P.


    Land cover classification is one of the most significant applications in remote sensing. Compared to optical sensing technologies, synthetic aperture radar (SAR) can penetrate through clouds and have all-weather capabilities. Therefore, land cover classification for SAR image is important in remote sensing. The subspace method is a novel method for the SAR data, which reduces data dimensionality by incorporating feature extraction into the classification process. This paper uses the averaged learning subspace method (ALSM) method that can be applied to the fully polarimetric SAR image for classification. The ALSM algorithm integrates three-component decomposition, eigenvalue/eigenvector decomposition and textural features derived from the gray-level cooccurrence matrix (GLCM). The study site, locates in the Dingxing county, in Hebei Province, China. We compare the subspace method with the traditional supervised Wishart classification. By conducting experiments on the fully polarimetric Radarsat-2 image, we conclude the proposed method yield higher classification accuracy. Therefore, the ALSM classification method is a feasible and alternative method for SAR image.

  13. A basis in an invariant subspace of analytic functions

    Krivosheev, A S [Institute of Mathematics with Computing Centre, Ufa Science Centre, Russian Academy of Sciences, Ufa (Russian Federation); Krivosheeva, O A [Bashkir State University, Ufa (Russian Federation)


    The existence problem for a basis in a differentiation-invariant subspace of analytic functions defined in a bounded convex domain in the complex plane is investigated. Conditions are found for the solvability of a certain special interpolation problem in the space of entire functions of exponential type with conjugate diagrams lying in a fixed convex domain. These underlie sufficient conditions for the existence of a basis in the invariant subspace. This basis consists of linear combinations of eigenfunctions and associated functions of the differentiation operator, whose exponents are combined into relatively small clusters. Necessary conditions for the existence of a basis are also found. Under a natural constraint on the number of points in the groups, these coincide with the sufficient conditions. That is, a criterion is found under this constraint that a basis constructed from relatively small clusters exists in an invariant subspace of analytic functions in a bounded convex domain in the complex plane. Bibliography: 25 titles.

  14. LESS: a model-based classifier for sparse subspaces.

    Veenman, Cor J; Tax, David M J


    In this paper, we specifically focus on high-dimensional data sets for which the number of dimensions is an order of magnitude higher than the number of objects. From a classifier design standpoint, such small sample size problems have some interesting challenges. The first challenge is to find, from all hyperplanes that separate the classes, a separating hyperplane which generalizes well for future data. A second important task is to determine which features are required to distinguish the classes. To attack these problems, we propose the LESS (Lowest Error in a Sparse Subspace) classifier that efficiently finds linear discriminants in a sparse subspace. In contrast with most classifiers for high-dimensional data sets, the LESS classifier incorporates a (simple) data model. Further, by means of a regularization parameter, the classifier establishes a suitable trade-off between subspace sparseness and classification accuracy. In the experiments, we show how LESS performs on several high-dimensional data sets and compare its performance to related state-of-the-art classifiers like, among others, linear ridge regression with the LASSO and the Support Vector Machine. It turns out that LESS performs competitively while using fewer dimensions.

  15. Discrete optimization

    Parker, R Gary


    This book treats the fundamental issues and algorithmic strategies emerging as the core of the discipline of discrete optimization in a comprehensive and rigorous fashion. Following an introductory chapter on computational complexity, the basic algorithmic results for the two major models of polynomial algorithms are introduced--models using matroids and linear programming. Further chapters treat the major non-polynomial algorithms: branch-and-bound and cutting planes. The text concludes with a chapter on heuristic algorithms.Several appendixes are included which review the fundamental ideas o

  16. Chebyshev polynomial filtered subspace iteration in the discontinuous Galerkin method for large-scale electronic structure calculations

    Banerjee, Amartya S.; Lin, Lin; Hu, Wei; Yang, Chao; Pask, John E.


    The Discontinuous Galerkin (DG) electronic structure method employs an adaptive local basis (ALB) set to solve the Kohn-Sham equations of density functional theory in a discontinuous Galerkin framework. The adaptive local basis is generated on-the-fly to capture the local material physics and can systematically attain chemical accuracy with only a few tens of degrees of freedom per atom. A central issue for large-scale calculations, however, is the computation of the electron density (and subsequently, ground state properties) from the discretized Hamiltonian in an efficient and scalable manner. We show in this work how Chebyshev polynomial filtered subspace iteration (CheFSI) can be used to address this issue and push the envelope in large-scale materials simulations in a discontinuous Galerkin framework. We describe how the subspace filtering steps can be performed in an efficient and scalable manner using a two-dimensional parallelization scheme, thanks to the orthogonality of the DG basis set and block-sparse structure of the DG Hamiltonian matrix. The on-the-fly nature of the ALB functions requires additional care in carrying out the subspace iterations. We demonstrate the parallel scalability of the DG-CheFSI approach in calculations of large-scale two-dimensional graphene sheets and bulk three-dimensional lithium-ion electrolyte systems. Employing 55 296 computational cores, the time per self-consistent field iteration for a sample of the bulk 3D electrolyte containing 8586 atoms is 90 s, and the time for a graphene sheet containing 11 520 atoms is 75 s.

  17. Maximally incompatible quantum observables

    Heinosaari, Teiko, E-mail: [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Schultz, Jussi, E-mail: [Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy); Toigo, Alessandro, E-mail: [Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Via Celoria 16, I-20133 Milano (Italy); Ziman, Mario, E-mail: [RCQI, Institute of Physics, Slovak Academy of Sciences, Dúbravská cesta 9, 84511 Bratislava (Slovakia); Faculty of Informatics, Masaryk University, Botanická 68a, 60200 Brno (Czech Republic)


    The existence of maximally incompatible quantum observables in the sense of a minimal joint measurability region is investigated. Employing the universal quantum cloning device it is argued that only infinite dimensional quantum systems can accommodate maximal incompatibility. It is then shown that two of the most common pairs of complementary observables (position and momentum; number and phase) are maximally incompatible.

  18. Computing approximate (block) rational Krylov subspaces without explicit inversion with extensions to symmetric matrices

    Mach, Thomas; Pranić, Miroslav S.; Vandebril, Raf


    It has been shown that approximate extended Krylov subspaces can be computed, under certain assumptions, without any explicit inversion or system solves. Instead, the vectors spanning the extended Krylov space are retrieved in an implicit way, via unitary similarity transformations, from an enlarged Krylov subspace. In this paper this approach is generalized to rational Krylov subspaces, which aside from poles at infinity and zero, also contain finite non-zero poles. Furthermore, the algorith...

  19. Discrete transforms

    Firth, Jean M


    The analysis of signals and systems using transform methods is a very important aspect of the examination of processes and problems in an increasingly wide range of applications. Whereas the initial impetus in the development of methods appropriate for handling discrete sets of data occurred mainly in an electrical engineering context (for example in the design of digital filters), the same techniques are in use in such disciplines as cardiology, optics, speech analysis and management, as well as in other branches of science and engineering. This text is aimed at a readership whose mathematical background includes some acquaintance with complex numbers, linear differen­ tial equations, matrix algebra, and series. Specifically, a familiarity with Fourier series (in trigonometric and exponential forms) is assumed, and an exposure to the concept of a continuous integral transform is desirable. Such a background can be expected, for example, on completion of the first year of a science or engineering degree cour...




    In this paper we formulate necessary and sufficient conditions for an arbitrary polyhedral set to be a positively invariant set of a linear discrete-time system. Polyhedral cones and linear subspaces are included in the analysis. A linear programming algorithm is presented that enables practical

  1. Chebyshev polynomial filtered subspace iteration in the Discontinuous Galerkin method for large-scale electronic structure calculations

    Banerjee, Amartya S; Hu, Wei; Yang, Chao; Pask, John E


    The Discontinuous Galerkin (DG) electronic structure method employs an adaptive local basis set to solve the equations of density functional theory in a discontinuous Galerkin framework. The methodology is implemented in the Discontinuous Galerkin Density Functional Theory (DGDFT) code for large-scale parallel electronic structure calculations. In DGDFT, the basis is generated on-the-fly to capture the local material physics, and can systematically attain chemical accuracy with only a few tens of degrees of freedom per atom. Hence, DGDFT combines the key advantage of planewave basis sets in terms of systematic improvability with that of localized basis sets in reducing basis size. A central issue for large-scale calculations, however, is the computation of the electron density from the discretized Hamiltonian in an efficient and scalable manner. We show in this work how Chebyshev polynomial filtered subspace iteration (CheFSI) can be used to address this issue and push the envelope in large-scale materials si...


    Zhenyue Zhang; Hongyuan Zha; Wenlong Ying


    We propose a quadratically convergent algorithm for computing the invariant subspaces of an Hermitian matrix.Each iteration of the algorithm consists of one matrix-matrix multiplication and one QR decomposition.We present an accurate convergence analysis of the algorithm without using the big O notation.We also propose a general framework based on implicit rational transformations which allows us to make connections with several existing algorithms and to derive classes of extensions to our basic algorithm with faster convergence rates.Several numerical examples are given which compare some aspects of the existing algorithms and the new Mgorithms.

  3. Genome classification by gene distribution: An overlapping subspace clustering approach

    Halgamuge Saman K


    Full Text Available Abstract Background Genomes of lower organisms have been observed with a large amount of horizontal gene transfers, which cause difficulties in their evolutionary study. Bacteriophage genomes are a typical example. One recent approach that addresses this problem is the unsupervised clustering of genomes based on gene order and genome position, which helps to reveal species relationships that may not be apparent from traditional phylogenetic methods. Results We propose the use of an overlapping subspace clustering algorithm for such genome classification problems. The advantage of subspace clustering over traditional clustering is that it can associate clusters with gene arrangement patterns, preserving genomic information in the clusters produced. Additionally, overlapping capability is desirable for the discovery of multiple conserved patterns within a single genome, such as those acquired from different species via horizontal gene transfers. The proposed method involves a novel strategy to vectorize genomes based on their gene distribution. A number of existing subspace clustering and biclustering algorithms were evaluated to identify the best framework upon which to develop our algorithm; we extended a generic subspace clustering algorithm called HARP to incorporate overlapping capability. The proposed algorithm was assessed and applied on bacteriophage genomes. The phage grouping results are consistent overall with the Phage Proteomic Tree and showed common genomic characteristics among the TP901-like, Sfi21-like and sk1-like phage groups. Among 441 phage genomes, we identified four significantly conserved distribution patterns structured by the terminase, portal, integrase, holin and lysin genes. We also observed a subgroup of Sfi21-like phages comprising a distinctive divergent genome organization and identified nine new phage members to the Sfi21-like genus: Staphylococcus 71, phiPVL108, Listeria A118, 2389, Lactobacillus phi AT3, A2

  4. Accurate Excited State Geometries within Reduced Subspace TDDFT/TDA.

    Robinson, David


    A method for the calculation of TDDFT/TDA excited state geometries within a reduced subspace of Kohn-Sham orbitals has been implemented and tested. Accurate geometries are found for all of the fluorophore-like molecules tested, with at most all valence occupied orbitals and half of the virtual orbitals included but for some molecules even fewer orbitals. Efficiency gains of between 15 and 30% are found for essentially the same level of accuracy as a standard TDDFT/TDA excited state geometry optimization calculation.

  5. Image segmentation by using the localized subspace iteration algorithm


    An image segmentation algorithm called"segmentation based on the localized subspace iterations"(SLSI)is proposed in this paper.The basic idea is to combine the strategies in Ncut algorithm by Shi and Malik in 2000 and the LSI by E,Li and Lu in 2007.The LSI is applied to solve an eigenvalue problem associated with the affinity matrix of an image,which makes the overall algorithm linearly scaled.The choices of the partition number,the supports and weight functions in SLSI are discussed.Numerical experiments for real images show the applicability of the algorithm.

  6. Subspace Distribution Clustering HMM for Chinese Digit Speech Recognition


    As a kind of statistical method, the technique of Hidden Markov Model (HMM) is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model (SDCHMM), derived from the Continuous Density Hidden Markov Model (CDHMM), is introduced. With parameter tying, a new method to train SDCHMMs is described. Compared with the conventional training method, an SDCHMM recognizer trained by means of the new method achieves higher accuracy and speed. Experiment results show that the SDCHMM recognizer outperforms the CDHMM recognizer on speech recognition of Chinese digits.

  7. Maximizers versus satisficers

    Parker, Andrew M.; Wandi Bruine de Bruin; Baruch Fischhoff


    Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bruine de Bruin et al., 2007). Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002), we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions...

  8. Active Sonar Detection in Reverberation via Signal Subspace Extraction Algorithm

    Ma Xiaochuan


    Full Text Available This paper presents a new algorithm called Signal Subspace Extraction (SSE for detecting and estimating target echoes in reverberation. The new algorithm can be taken as an extension of the Principal Component Inverse (PCI and maintains the benefit of PCI algorithm and moreover shows better performance due to a more reasonable reverberation model. In the SSE approach, a best low-rank estimate of a target echo is extracted by decomposing the returns into short duration subintervals and by invoking the Eckart-Young theorem twice. It was assumed that CW is less efficiency in lower Doppler than broadband waveforms in spectrum methods; however, the subspace methods show good performance in detection whatever the respective Doppler is. Hence, the signal emitted by active sonar is CW in the new algorithm which performs well in detection and estimation even when low Doppler is low. Further, a block forward matrix is proposed to extend the algorithm to the sensor array problem. The comparison among the block forward matrix, the conventional matrix, and the three-mode array is discussed. Echo separation is also provided by the new algorithm. Examples are presented using both real, active-sonar data and simulated data.

  9. A Subspace Method for Dynamical Estimation of Evoked Potentials

    Stefanos D. Georgiadis


    Full Text Available It is a challenge in evoked potential (EP analysis to incorporate prior physiological knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP characteristics estimation. Prior information about phase-locked properties of the EPs is assesed by means of estimated signal subspace and eigenvalue decomposition. Then for those situations that dynamic fluctuations from stimulus-to-stimulus could be expected, prior information can be exploited by means of state-space modeling and recursive Bayesian mean square estimation methods (Kalman filtering and smoothing. We demonstrate that a few dominant eigenvectors of the data correlation matrix are able to model trend-like changes of some component of the EPs, and that Kalman smoother algorithm is to be preferred in terms of better tracking capabilities and mean square error reduction. We also demonstrate the effect of strong artifacts, particularly eye blinks, on the quality of the signal subspace and EP estimates by means of independent component analysis applied as a prepossessing step on the multichannel measurements.

  10. Conformal Laplace superintegrable systems in 2D: polynomial invariant subspaces

    Escobar-Ruiz, M. A.; Miller, Willard, Jr.


    2nd-order conformal superintegrable systems in n dimensions are Laplace equations on a manifold with an added scalar potential and 2n-1 independent 2nd order conformal symmetry operators. They encode all the information about Helmholtz (eigenvalue) superintegrable systems in an efficient manner: there is a 1-1 correspondence between Laplace superintegrable systems and Stäckel equivalence classes of Helmholtz superintegrable systems. In this paper we focus on superintegrable systems in two-dimensions, n = 2, where there are 44 Helmholtz systems, corresponding to 12 Laplace systems. For each Laplace equation we determine the possible two-variate polynomial subspaces that are invariant under the action of the Laplace operator, thus leading to families of polynomial eigenfunctions. We also study the behavior of the polynomial invariant subspaces under a Stäckel transform. The principal new results are the details of the polynomial variables and the conditions on parameters of the potential corresponding to polynomial solutions. The hidden gl 3-algebraic structure is exhibited for the exact and quasi-exact systems. For physically meaningful solutions, the orthogonality properties and normalizability of the polynomials are presented as well. Finally, for all Helmholtz superintegrable solvable systems we give a unified construction of one-dimensional (1D) and two-dimensional (2D) quasi-exactly solvable potentials possessing polynomial solutions, and a construction of new 2D PT-symmetric potentials is established.

  11. Hyponormal differential operators with discrete spectrum

    Zameddin I. Ismailov


    Full Text Available In this work, we first describe all the maximal hyponormal extensions of a minimal operator generated by a linear differential-operator expression of the first-order in the Hilbert space of vector-functions in a finite interval. Next, we investigate the discreteness of the spectrum and the asymptotical behavior of the modules of the eigenvalues for these maximal hyponormal extensions.

  12. On Maximal Injectivity

    Ming Yi WANG; Guo ZHAO


    A right R-module E over a ring R is said to be maximally injective in case for any maximal right ideal m of R, every R-homomorphism f : m → E can be extended to an R-homomorphism f' : R → E. In this paper, we first construct an example to show that maximal injectivity is a proper generalization of injectivity. Then we prove that any right R-module over a left perfect ring R is maximally injective if and only if it is injective. We also give a partial affirmative answer to Faith's conjecture by further investigating the property of maximally injective rings. Finally, we get an approximation to Faith's conjecture, which asserts that every injective right R-module over any left perfect right self-injective ring R is the injective hull of a projective submodule.

  13. Maximizers versus satisficers

    Andrew M. Parker


    Full Text Available Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bruine de Bruin et al., 2007. Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002, we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions, more avoidance of decision making, and greater tendency to experience regret. Contrary to predictions, self-reported maximizers were more likely to report spontaneous decision making. However, the relationship between self-reported maximizing and worse life outcomes is largely unaffected by controls for measures of other decision-making styles, decision-making competence, and demographic variables.

  14. On Maximal Green Sequences

    Brüstle, Thomas; Pérotin, Matthieu


    Maximal green sequences are particular sequences of quiver mutations which were introduced by Keller in the context of quantum dilogarithm identities and independently by Cecotti-Cordova-Vafa in the context of supersymmetric gauge theory. Our aim is to initiate a systematic study of these sequences from a combinatorial point of view. Interpreting maximal green sequences as paths in various natural posets arising in representation theory, we prove the finiteness of the number of maximal green sequences for cluster finite quivers, affine quivers and acyclic quivers with at most three vertices. We also give results concerning the possible numbers and lengths of these maximal green sequences. Finally we describe an algorithm for computing maximal green sequences for arbitrary valued quivers which we used to obtain numerous explicit examples that we present.

  15. Outlier Ranking via Subspace Analysis in Multiple Views of the Data

    Muller, Emmanuel; Assent, Ira; Iglesias, Patricia


    , a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its...


    徐徽宁; 孙麟平


    We consider an Iterated-Subspace Minimization(ISM) method for solving large-scale unconstrained minimization problems. At each major iteration of the method,a two-dimensional manifold, the iterated subspace, is constructed and an approximate minimizer of the objective function in this manifold then determined, and a symmetric rank-one updating is used to solve the inner minimization problem.

  17. Crystallizing highly-likely subspaces that contain an unknown quantum state of light

    Teo, Yong Siah; Mogilevtsev, Dmitri; Mikhalychev, Alexander; Řeháček, Jaroslav; Hradil, Zdeněk


    In continuous-variable tomography, with finite data and limited computation resources, reconstruction of a quantum state of light is performed on a finite-dimensional subspace. In principle, the data themselves encode all information about the relevant subspace that physically contains the state. We provide a straightforward and numerically feasible procedure to uniquely determine the appropriate reconstruction subspace by extracting this information directly from the data for any given unknown quantum state of light and measurement scheme. This procedure makes use of the celebrated statistical principle of maximum likelihood, along with other validation tools, to grow an appropriate seed subspace into the optimal reconstruction subspace, much like the nucleation of a seed into a crystal. Apart from using the available measurement data, no other assumptions about the source or preconceived parametric model subspaces are invoked. This ensures that no spurious reconstruction artifacts are present in state reconstruction as a result of inappropriate choices of the reconstruction subspace. The procedure can be understood as the maximum-likelihood reconstruction for quantum subspaces, which is an analog to, and fully compatible with that for quantum states.

  18. Subspace in Linear Algebra: Investigating Students' Concept Images and Interactions with the Formal Definition

    Wawro, Megan; Sweeney, George F.; Rabin, Jeffrey M.


    This paper reports on a study investigating students' ways of conceptualizing key ideas in linear algebra, with the particular results presented here focusing on student interactions with the notion of subspace. In interviews conducted with eight undergraduates, we found students' initial descriptions of subspace often varied substantially from…

  19. Time stepping free numerical solution of linear differential equations: Krylov subspace versus waveform relaxation

    Bochev, Mikhail A.; Oseledets, I.V.; Tyrtyshnikov, E.E.


    The aim of this paper is two-fold. First, we propose an efficient implementation of the continuous time waveform relaxation method based on block Krylov subspaces. Second, we compare this new implementation against Krylov subspace methods combined with the shift and invert technique.

  20. Iterative across-time solution of linear differential equations: Krylov subspace versus waveform relaxation

    Bochev, Mikhail A.; Oseledets, I.V.; Tyrtyshnikov, E.E.

    The aim of this paper is two-fold. First, we propose an efficient implementation of the continuous time waveform relaxation (WR) method based on block Krylov subspaces. Second, we compare this new WR-Krylov implementation against Krylov subspace methods combined with the shift and invert (SAI)

  1. Radio astronomical image formation using constrained least squares and Krylov subspaces

    Mouri Sardarabadi, Ahmad; Leshem, Amir; van der Veen, Alle-Jan


    Aims: Image formation for radio astronomy can be defined as estimating the spatial intensity distribution of celestial sources throughout the sky, given an array of antennas. One of the challenges with image formation is that the problem becomes ill-posed as the number of pixels becomes large. The introduction of constraints that incorporate a priori knowledge is crucial. Methods: In this paper we show that in addition to non-negativity, the magnitude of each pixel in an image is also bounded from above. Indeed, the classical "dirty image" is an upper bound, but a much tighter upper bound can be formed from the data using array processing techniques. This formulates image formation as a least squares optimization problem with inequality constraints. We propose to solve this constrained least squares problem using active set techniques, and the steps needed to implement it are described. It is shown that the least squares part of the problem can be efficiently implemented with Krylov-subspace-based techniques. We also propose a method for correcting for the possible mismatch between source positions and the pixel grid. This correction improves both the detection of sources and their estimated intensities. The performance of these algorithms is evaluated using simulations. Results: Based on parametric modeling of the astronomical data, a new imaging algorithm based on convex optimization, active sets, and Krylov-subspace-based solvers is presented. The relation between the proposed algorithm and sequential source removing techniques is explained, and it gives a better mathematical framework for analyzing existing algorithms. We show that by using the structure of the algorithm, an efficient implementation that allows massive parallelism and storage reduction is feasible. Simulations are used to compare the new algorithm to classical CLEAN. Results illustrate that for a discrete point model, the proposed algorithm is capable of detecting the correct number of sources

  2. Discretization of topological spaces

    Amini, Massoud; Golestani, Nasser


    There are several compactification procedures in topology, but there is only one standard discretization, namely, replacing the original topology with the discrete topology. We give a notion of discretization which is dual (in categorical sense) to compactification and give examples of discretizations. Especially, a discretization functor from the category of $\\alpha$-scattered Stonean spaces to the category of discrete spaces is constructed which is the converse of the Stone-\\v{C}ech compact...

  3. Energy Band Calculations for Maximally Even Superlattices

    Krantz, Richard; Byrd, Jason


    Superlattices are multiple-well, semiconductor heterostructures that can be described by one-dimensional potential wells separated by potential barriers. We refer to a distribution of wells and barriers based on the theory of maximally even sets as a maximally even superlattice. The prototypical example of a maximally even set is the distribution of white and black keys on a piano keyboard. Black keys may represent wells and the white keys represent barriers. As the number of wells and barriers increase, efficient and stable methods of calculation are necessary to study these structures. We have implemented a finite-element method using the discrete variable representation (FE-DVR) to calculate E versus k for these superlattices. Use of the FE-DVR method greatly reduces the amount of calculation necessary for the eigenvalue problem.

  4. Discrete Curvatures and Discrete Minimal Surfaces

    Sun, Xiang


    This thesis presents an overview of some approaches to compute Gaussian and mean curvature on discrete surfaces and discusses discrete minimal surfaces. The variety of applications of differential geometry in visualization and shape design leads to great interest in studying discrete surfaces. With the rich smooth surface theory in hand, one would hope that this elegant theory can still be applied to the discrete counter part. Such a generalization, however, is not always successful. While discrete surfaces have the advantage of being finite dimensional, thus easier to treat, their geometric properties such as curvatures are not well defined in the classical sense. Furthermore, the powerful calculus tool can hardly be applied. The methods in this thesis, including angular defect formula, cotangent formula, parallel meshes, relative geometry etc. are approaches based on offset meshes or generalized offset meshes. As an important application, we discuss discrete minimal surfaces and discrete Koenigs meshes.

  5. Maximal Attractors for the m-Dimensional Cahn-Hilliard System

    Wei Nian ZHANG


    In this paper we discuss maximal attractors of the m-dimensional Cahn-Hilliard System in the product spaces (L2(Ω))m and (H2(Ω))m in terms of D. Henry's general theory and from the viewpoint of compactness and absorptivity of semigroups as R. Temam did. After giving the existence and uniqueness of global solutions, we technically restrict our discussion to some subspaces, give estimates with a new graph norm, and obtain the existence of maximal attractors and some properties of them.

  6. Geometry of discrete quantum computing

    Hanson, Andrew J.; Ortiz, Gerardo; Sabry, Amr; Tai, Yu-Tsung


    Conventional quantum computing entails a geometry based on the description of an n-qubit state using 2n infinite precision complex numbers denoting a vector in a Hilbert space. Such numbers are in general uncomputable using any real-world resources, and, if we have the idea of physical law as some kind of computational algorithm of the universe, we would be compelled to alter our descriptions of physics to be consistent with computable numbers. Our purpose here is to examine the geometric implications of using finite fields Fp and finite complexified fields \\mathbf {F}_{p^2} (based on primes p congruent to 3 (mod4)) as the basis for computations in a theory of discrete quantum computing, which would therefore become a computable theory. Because the states of a discrete n-qubit system are in principle enumerable, we are able to determine the proportions of entangled and unentangled states. In particular, we extend the Hopf fibration that defines the irreducible state space of conventional continuous n-qubit theories (which is the complex projective space \\mathbf {CP}^{2^{n}-1}) to an analogous discrete geometry in which the Hopf circle for any n is found to be a discrete set of p + 1 points. The tally of unit-length n-qubit states is given, and reduced via the generalized Hopf fibration to \\mathbf {DCP}^{2^{n}-1}, the discrete analogue of the complex projective space, which has p^{2^{n}-1} (p-1)\\,\\prod _{k=1}^{n-1} ( p^{2^{k}}+1) irreducible states. Using a measure of entanglement, the purity, we explore the entanglement features of discrete quantum states and find that the n-qubit states based on the complexified field \\mathbf {F}_{p^2} have pn(p - 1)n unentangled states (the product of the tally for a single qubit) with purity 1, and they have pn + 1(p - 1)(p + 1)n - 1 maximally entangled states with purity zero.

  7. Once Again, Maxims

    Rudiger Bubner


    Full Text Available Even though the maxims' theory is not at thecenter of Kant's ethics, it is the unavoidable basis of the categoric imperative's formulation. Kant leanson the transmitted representations of modem moral theory. During the last decades, the notion of maxims has deserved more attention, due to the philosophy of language's debates on rules, and due to action theory's interest in this notion. I here by brietly expound my views in these discussions.

  8. Hausdorff hyperspaces of $R^m$ and their dense subspaces

    Kubis, Wieslaw


    Let $CLB_H(X)$ denote the hyperspace of closed bounded subsets of a metric space $X$, endowed with the Hausdorff metric topology. We prove, among others, that natural dense subspaces of $CLB_H(R^m)$ of all nowhere dense closed sets, of all perfect sets, of all Cantor sets and of all Lebesgue measure zero sets are homeomorphic to the Hilbert space $\\ell_2$. Moreover, we investigate the hyperspace $CL_H(R)$ of all nonempty closed subsets of the real line $R$ with the Hausdorff (infinite-valued) metric. We show that a nonseparable component of $CL_H(R)$ is homeomorphic to the Hilbert space $\\ell_2(2^{\\aleph_0})$ as long as it does not contain any of the sets $R, [0,\\infty), (-\\infty,0]$.

  9. Universal quantum computation in waveguide QED using decoherence free subspaces

    Paulisch, V.; Kimble, H. J.; González-Tudela, A.


    The interaction of quantum emitters with one-dimensional photon-like reservoirs induces strong and long-range dissipative couplings that give rise to the emergence of the so-called decoherence free subspaces (DFSs) which are decoupled from dissipation. When introducing weak perturbations on the emitters, e.g., driving, the strong collective dissipation enforces an effective coherent evolution within the DFS. In this work, we show explicitly how by introducing single-site resolved drivings, we can use the effective dynamics within the DFS to design a universal set of one and two-qubit gates within the DFS of an ensemble of two-level atom-like systems. Using Liouvillian perturbation theory we calculate the scaling with the relevant figures of merit of the systems, such as the Purcell factor and imperfect control of the drivings. Finally, we compare our results with previous proposals using atomic Λ systems in leaky cavities.

  10. Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

    Pham Tuan D


    Full Text Available Abstract Background Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM, which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting. Results Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The

  11. Analysis and Improvement of Low Rank Representation for Subspace segmentation

    Siming, Wei


    We analyze and improve low rank representation (LRR), the state-of-the-art algorithm for subspace segmentation of data. We prove that for the noiseless case, the optimization model of LRR has a unique solution, which is the shape interaction matrix (SIM) of the data matrix. So in essence LRR is equivalent to factorization methods. We also prove that the minimum value of the optimization model of LRR is equal to the rank of the data matrix. For the noisy case, we show that LRR can be approximated as a factorization method that combines noise removal by column sparse robust PCA. We further propose an improved version of LRR, called Robust Shape Interaction (RSI), which uses the corrected data as the dictionary instead of the noisy data. RSI is more robust than LRR when the corruption in data is heavy. Experiments on both synthetic and real data testify to the improved robustness of RSI.

  12. Universal Fault-Tolerant Computation on Decoherence-Free Subspaces

    Bacon, D J; Lidar, D A; Whaley, K B


    A general scheme to perform universal quantum computation fault-tolerantly within decoherence-free subspaces (DFSs) of a system's Hilbert space is derived. This scheme leads to the first fault-tolerant realization of universal quantum computation on DFSs with the properties that (i) only one- and two-qubit interactions are required, and (ii) the system remains within the DFS throughout the entire implementation of a quantum gate. We show explicitly how to perform universal computation on clusters of the four-qubit DFS encoding one logical qubit each under "collective decoherence" (qubit-permutation-invariant system-bath coupling). Our results have immediate relevance to a number of proposed quantum computer implementations, in particular those in which the internal system Hamiltonian is of the Heisenberg type, such as spin-spin coupled quantum dots.

  13. Subspace identification and classification of healthy human gait.

    Vinzenz von Tscharner

    Full Text Available PURPOSE: The classification between different gait patterns is a frequent task in gait assessment. The base vectors were usually found using principal component analysis (PCA is replaced by an iterative application of the support vector machine (SVM. The aim was to use classifyability instead of variability to build a subspace (SVM space that contains the information about classifiable aspects of a movement. The first discriminant of the SVM space will be compared to a discriminant found by an independent component analysis (ICA in the SVM space. METHODS: Eleven runners ran using shoes with different midsoles. Kinematic data, representing the movements during stance phase when wearing the two shoes, was used as input to a PCA and SVM. The data space was decomposed by an iterative application of the SVM into orthogonal discriminants that were able to classify the two movements. The orthogonal discriminants spanned a subspace, the SVM space. It represents the part of the movement that allowed classifying the two conditions. The data in the SVM space was reconstructed for a visual assessment of the movement difference. An ICA was applied to the data in the SVM space to obtain a single discriminant. Cohen's d effect size was used to rank the PCA vectors that could be used to classify the data, the first SVM discriminant or the ICA discriminant. RESULTS: The SVM base contains all the information that discriminates the movement of the two shod conditions. It was shown that the SVM base contains some redundancy and a single ICA discriminant was found by applying an ICA in the SVM space. CONCLUSIONS: A combination of PCA, SVM and ICA is best suited to extract all parts of the gait pattern that discriminates between the two movements and to find a discriminant for the classification of dichotomous kinematic data.

  14. Self-adjoint Time Operator is the Rule for Discrete Semibounded Hamiltonians

    Galapon, E A


    We prove explicitly that to every discrete, semibounded Hamiltonian with constant degeneracy and with finite sum of the squares of the reciprocal of its eigenvalues and whose eigenvectors span the entire Hilbert space there exists a characteristic self-adjoint time operator which is canonically conjugate to the Hamiltonian in a dense subspace of the Hilbert space. Moreover, we show that each characteristic time operator generates an uncountable class of self- adjoint operators canonically conjugate with the same Hamiltonian.

  15. Error analysis of Padé iterations for computing matrix invariant subspaces

    Zhenyue ZHANG; Rui HE


    The method of Padé matrix iteration is commonly used for computing matrix sign function and invariant subspaces of a real or complex matrix. In this paper, a detailed rounding error analysis is given for two classical schemes of the Padé matrix iteration, using basic matrix floating point arithmetics. Error estimations of computing invariant sub-spaces by the Padé sign iteration are also provided. Numerical experiments are given to show the numerical behaviors of the Padé iterations and the corresponding subspace computation.


    Andrzej Puchalski


    Full Text Available System diagnostics based on vibroacoustics signals, carried out by means of stochastic subspace methods was undertaken in the hereby paper. Subspace methods are the ones based on numerical linear algebra tools. The considered solutions belong to diagnostic methods according to data, leading to the generation of residuals allowing failure recognition of elements and assemblies in machines and devices. The algorithm of diagnostics according to the subspace observation method was applied – in the paper – for the estimation of the valve system of the spark ignition engine.

  17. The four-qubit singlet state and decoherence-free subspaces

    Cabello, A


    It is pointed out that the recent experimental preparation of the four-qubit singlet state by Weinfurter's group is a fundamental achievement for the encoding of quantum information in decoherence-free (DF) subspaces. This state is the DF state orthogonal to the tensor product of two two-qubit singlet states, whose DF properties were experimentally checked by P. G. Kwiat et al. [Science 290, 498 (2000)], and thus provides the missing state for the simplest nontrivial encoding of quantum information in a DF subspace. An experiment to study this DF subspace is suggested.



    This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR.Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.

  19. Discretization of Continuous Frame

    A Fattahi; H Javanshiri


    In this paper we consider the notion of continuous frame of subspaces and define a new concept of continuous frame, entitled continuous atomic resolution of identity, for arbitrary Hilbert space $\\mathcal{H}$ which has a countable reconstruction formula. Among the other results, we characterize the relationship between this new concept and other known continuous frames. Finally, we state and prove the assertions of the stability of perturbation in this concept.

  20. Groupoids, Discrete Mechanics, and Discrete Variation

    GUO Jia-Feng; JIA Xiao-Yu; WU Ke; ZHAO Wei-Zhong


    After introducing some of the basic definitions and results from the theory of groupoid and Lie algebroid,we investigate the discrete Lagrangian mechanics from the viewpoint of groupoid theory and give the connection between groupoids variation and the methods of the first and second discrete variational principles.

  1. On discrete cosine transform

    Zhou, Jianqin


    The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases.

  2. Mimetic discretization methods

    Castillo, Jose E


    To help solve physical and engineering problems, mimetic or compatible algebraic discretization methods employ discrete constructs to mimic the continuous identities and theorems found in vector calculus. Mimetic Discretization Methods focuses on the recent mimetic discretization method co-developed by the first author. Based on the Castillo-Grone operators, this simple mimetic discretization method is invariably valid for spatial dimensions no greater than three. The book also presents a numerical method for obtaining corresponding discrete operators that mimic the continuum differential and

  3. Discrete mathematics, discrete physics and numerical methods

    Felice Iavernaro


    Full Text Available Discrete mathematics has been neglected for a long time. It has been put in the shade by the striking success of continuous mathematics in the last two centuries, mainly because continuous models in physics proved very reliable, but also because of the greater difficulty in dealing with it. This perspective has been rapidly changing in the last years owing to the needs of the numerical analysis and, more recently, of the so called discrete physics. In this paper, starting from some sentences of Fichera about discrete and continuous world, we shall present some considerations about discrete phenomena which arise when designing numerical methods or discrete models for some classical physical problems.

  4. Reflection Quasilattices and the Maximal Quasilattice

    Boyle, Latham


    We introduce the concept of a {\\it reflection quasilattice}, the quasiperiodic generalization of a Bravais lattice with irreducible reflection symmetry. Among their applications, reflection quasilattices are the reciprocal (i.e. Bragg diffraction) lattices for quasicrystals and quasicrystal tilings, such as Penrose tilings, with irreducible reflection symmetry and discrete scale invariance. In a follow-up paper, we will show that reflection quasilattices can be used to generate tilings in real space with properties analogous to those in Penrose tilings, but with different symmetries and in various dimensions. Here we prove that reflection quasilattices only exist in dimensions two, three and four, and we prove that there is a unique reflection quasilattice in dimension four: the "maximal reflection quasilattice" in terms of dimensionality and symmetry. We further show that, unlike crystallographic Bravais lattices, all reflection quasilattices are invariant under rescaling by certain discrete scale factors. W...



  6. Face Recognition of Illumination Tolerance in 2D Subspace Based on the Optimum Correlation Filter

    Yi Xu


    ... subspace based on the optimal correlation filter. Firstly, through the use of a particular class 2D-PCA the face image is reconstructed and by using the optimum projecting image correlation filter (OPICF...

  7. Performance Comparison of Reconstruction Algorithms in Discrete Blind Multi-Coset Sampling

    Grigoryan, Ruben; Arildsen, Thomas; Tandur, Deepaknath


    This paper investigates the performance of different reconstruction algorithms in discrete blind multi-coset sampling. Multi-coset scheme is a promising compressed sensing architecture that can replace traditional Nyquist-rate sampling in the applications with multi-band frequency sparse signals....... The performance of the existing compressed sensing reconstruction algorithms have not been investigated yet for the discrete multi-coset sampling. We compare the following algorithms – orthogonal matching pursuit, multiple signal classification, subspace-augmented multiple signal classification, focal under...

  8. Closed and Open Loop Subspace System Identification of the Kalman Filter

    David Di Ruscio


    Full Text Available Some methods for consistent closed loop subspace system identification presented in the literature are analyzed and compared to a recently published subspace algorithm for both open as well as for closed loop data, the DSR_e algorithm. Some new variants of this algorithm are presented and discussed. Simulation experiments are included in order to illustrate if the algorithms are variance efficient or not.

  9. A Subspace Embedding Method in L2 Norm via Fast Cauchy Transform

    Xu Xiang


    Full Text Available We propose a subspace embedding method via Fast Cauchy Transform (FCT in L2 norm. It is motivated by and complements the work of the subspace embedding method in Lp norm, for all p∈[1,∞] except p = 2, by K. L. Clarkson (ACM-SIAM, 2013. Unlike the traditionally used orthogonal basis in Johnson-Lindenstrauss (JL embedding, we employ the well-conditioned basis in L2 norm to obtain concentration property of FCT in L2 norm.

  10. A New Subspace Correction Method for Nonlinear Unconstrained Convex Optimization Problems

    Rong-liang CHEN; Jin-ping ZENG


    This paper gives a new subspace correction algorithm for nonlinear unconstrained convex optimization problems based on the multigrid approach proposed by S.Nash in 2000 and the subspace correction algorithm proposed by X.Tai and J.Xu in 2001.Under some reasonable assumptions,we obtain the convergence as well as a convergence rate estimate for the algorithm.Numerical results show that the algorithm is effective.


    侯绳照; 胡俊云


    Let M be an approximately finite codimensional quasi-invariant subspace of the Fock space.This paper gives a formula to calculate the codimension of such spaces and uses this formulato study the structure of quasi-invariant subspaces of the Fock space. Especially, as one ofapplications, it is showed that the analogue of Beurling's theorem is not true for the Fock spaceL2a (Cn ) in the case of n ≥ 2.

  12. Subspace orthogonalization for substructuring preconditioners for nonsymmetric systems of linear equations

    Starke, G. [Universitaet Karlsruhe (Germany)


    For nonselfadjoint elliptic boundary value problems which are preconditioned by a substructuring method, i.e., nonoverlapping domain decomposition, the author introduces and studies the concept of subspace orthogonalization. In subspace orthogonalization variants of Krylov methods the computation of inner products and vector updates, and the storage of basis elements is restricted to a (presumably small) subspace, in this case the edge and vertex unknowns with respect to the partitioning into subdomains. The author investigates subspace orthogonalization for two specific iterative algorithms, GMRES and the full orthogonalization method (FOM). This is intended to eliminate certain drawbacks of the Arnoldi-based Krylov subspace methods mentioned above. Above all, the length of the Arnoldi recurrences grows linearly with the iteration index which is therefore restricted to the number of basis elements that can be held in memory. Restarts become necessary and this often results in much slower convergence. The subspace orthogonalization methods, in contrast, require the storage of only the edge and vertex unknowns of each basis element which means that one can iterate much longer before restarts become necessary. Moreover, the computation of inner products is also restricted to the edge and vertex points which avoids the disturbance of the computational flow associated with the solution of subdomain problems. The author views subspace orthogonalization as an alternative to restarting or truncating Krylov subspace methods for nonsymmetric linear systems of equations. Instead of shortening the recurrences, one restricts them to a subset of the unknowns which has to be carefully chosen in order to be able to extend this partial solution to the entire space. The author discusses the convergence properties of these iteration schemes and its advantages compared to restarted or truncated versions of Krylov methods applied to the full preconditioned system.

  13. Discrete Wigner function dynamics

    Klimov, A B; Munoz, C [Departamento de Fisica, Universidad de Guadalajara, Revolucion 1500, 44410, Guadalajara, Jalisco (Mexico)


    We study the evolution of the discrete Wigner function for prime and the power of prime dimensions using the discrete version of the star-product operation. Exact and semiclassical dynamics in the limit of large dimensions are considered.

  14. Fast filtering false active subspaces for efficient high dimensional similarity processing

    WANG GuoRen; YU Ge; XIN JunChang; ZHAO YuHai; ZHANG EnDe


    The query space of a similarity query is usually narrowed down by pruning inactive query subspaces which contain no query results and keeping active query subspaces which may contain objects corre-sponding to the request. However, some active query subspaces may contain no query results at all, those are called false active query subspaces. It is obvious that the performance of query processing degrades in the presence of false active query subspaces. Our experiments show that this problem becomes seriously when the data are high dimensional and the number of accesses to false active sub-spaces increases as the dimensionality increases. In order to solve this problem, this paper proposes a space mapping approach to reducing such unnecessary accesses. A given query space can be re-fined by filtering within its mapped space. To do so, a mapping strategy called maxgap is proposed to improve the efficiency of the refinement processing. Based on the mapping strategy, an index structure called MS-tree and algorithms of query processing are presented in this paper. Finally, the performance of MS-tree is compared with that of other competitors in terms of range queries on a real data set.

  15. Spectral Gauss quadrature method with subspace interpolation for Kohn-Sham Density functional theory

    Wang, Xin

    Algorithms with linear-scaling ( (N)) computational complexity for Kohn-Sham density functional theory (K-S DFT) is crucial for studying molecular systems beyond thousands of atoms. Of the  (N) methods that use a polynomial-based approximation of the density matrix, the linear-scaling spectral Gauss quadrature (LSSGQ) method (Suryanarayana et al., JMPS, 2013) has been shown to exhibit the fastest convergence. The LSSGQ method requires a Lanczos procedure at every node in a real-space mesh, leading to a large computational pre-factor. We propose a new interpolation scheme specific to the LSSGQ method that lift the need to perform a Lanczos procedure at every node in the real-mesh. This interpolation will be referred to as subspace interpolation. The key idea behind subspace interpolation is that there is a large overlap in the Krylov-subspaces produced by the Lanczos procedures of nodes that are close in real-space. The subspace interpolation scheme takes advantage of the block-Lanczos procedure to group the Krylov-subspaces from a few representative nodes to approximate the density matrix over a large collection of nodes. Subspace interpolation outperforms cubic-spline interpolation by several orders of magnitude.

  16. Subspace Leakage Analysis and Improved DOA Estimation With Small Sample Size

    Shaghaghi, Mahdi; Vorobyov, Sergiy A.


    Classical methods of DOA estimation such as the MUSIC algorithm are based on estimating the signal and noise subspaces from the sample covariance matrix. For a small number of samples, such methods are exposed to performance breakdown, as the sample covariance matrix can largely deviate from the true covariance matrix. In this paper, the problem of DOA estimation performance breakdown is investigated. We consider the structure of the sample covariance matrix and the dynamics of the root-MUSIC algorithm. The performance breakdown in the threshold region is associated with the subspace leakage where some portion of the true signal subspace resides in the estimated noise subspace. In this paper, the subspace leakage is theoretically derived. We also propose a two-step method which improves the performance by modifying the sample covariance matrix such that the amount of the subspace leakage is reduced. Furthermore, we introduce a phenomenon named as root-swap which occurs in the root-MUSIC algorithm in the low sample size region and degrades the performance of the DOA estimation. A new method is then proposed to alleviate this problem. Numerical examples and simulation results are given for uncorrelated and correlated sources to illustrate the improvement achieved by the proposed methods. Moreover, the proposed algorithms are combined with the pseudo-noise resampling method to further improve the performance.

  17. Dual signal subspace projection (DSSP): a novel algorithm for removing large interference in biomagnetic measurements

    Sekihara, Kensuke; Kawabata, Yuya; Ushio, Shuta; Sumiya, Satoshi; Kawabata, Shigenori; Adachi, Yoshiaki; Nagarajan, Srikantan S.


    Objective. In functional electrophysiological imaging, signals are often contaminated by interference that can be of considerable magnitude compared to the signals of interest. This paper proposes a novel algorithm for removing such interferences that does not require separate noise measurements. Approach. The algorithm is based on a dual definition of the signal subspace in the spatial- and time-domains. Since the algorithm makes use of this duality, it is named the dual signal subspace projection (DSSP). The DSSP algorithm first projects the columns of the measured data matrix onto the inside and outside of the spatial-domain signal subspace, creating a set of two preprocessed data matrices. The intersection of the row spans of these two matrices is estimated as the time-domain interference subspace. The original data matrix is projected onto the subspace that is orthogonal to this interference subspace. Main results. The DSSP algorithm is validated by using the computer simulation, and using two sets of real biomagnetic data: spinal cord evoked field data measured from a healthy volunteer and magnetoencephalography data from a patient with a vagus nerve stimulator. Significance. The proposed DSSP algorithm is effective for removing overlapped interference in a wide variety of biomagnetic measurements.

  18. Occlusion Handling via Random Subspace Classifiers for Human Detection.

    Marín, Javier; Vázquez, David; López, Antonio M; Amores, Jaume; Kuncheva, Ludmila I


    This paper describes a general method to address partial occlusions for human detection in still images. The random subspace method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach's capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labeling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes.

  19. Face image modeling by multilinear subspace analysis with missing values.

    Geng, Xin; Smith-Miles, Kate; Zhou, Zhi-Hua; Wang, Liang


    Multilinear subspace analysis (MSA) is a promising methodology for pattern-recognition problems due to its ability in decomposing the data formed from the interaction of multiple factors. The MSA requires a large training set, which is well organized in a single tensor, which consists of data samples with all possible combinations of the contributory factors. However, such a "complete" training set is difficult (or impossible) to obtain in many real applications. The missing-value problem is therefore crucial to the practicality of the MSA but has been hardly investigated up to present. To solve the problem, this paper proposes an algorithm named M(2)SA, which is advantageous in real applications due to the following: 1) it inherits the ability of the MSA to decompose the interlaced semantic factors; 2) it does not depend on any assumptions on the data distribution; and 3) it can deal with a high percentage of missing values. M(2)SA is evaluated by face image modeling on two typical multifactorial applications, i.e., face recognition and facial age estimation. Experimental results show the effectiveness of M(2) SA even when the majority of the values in the training tensor are missing.

  20. Optimal Design of Large Dimensional Adaptive Subspace Detectors

    Ben Atitallah, Ismail


    This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

  1. MODAL TRACKING of A Structural Device: A Subspace Identification Approach

    Candy, J. V. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Franco, S. N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Ruggiero, E. L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Emmons, M. C. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Lopez, I. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Stoops, L. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)


    Mechanical devices operating in an environment contaminated by noise, uncertainties, and extraneous disturbances lead to low signal-to-noise-ratios creating an extremely challenging processing problem. To detect/classify a device subsystem from noisy data, it is necessary to identify unique signatures or particular features. An obvious feature would be resonant (modal) frequencies emitted during its normal operation. In this report, we discuss a model-based approach to incorporate these physical features into a dynamic structure that can be used for such an identification. The approach we take after pre-processing the raw vibration data and removing any extraneous disturbances is to obtain a representation of the structurally unknown device along with its subsystems that capture these salient features. One approach is to recognize that unique modal frequencies (sinusoidal lines) appear in the estimated power spectrum that are solely characteristic of the device under investigation. Therefore, the objective of this effort is based on constructing a black box model of the device that captures these physical features that can be exploited to “diagnose” whether or not the particular device subsystem (track/detect/classify) is operating normally from noisy vibrational data. Here we discuss the application of a modern system identification approach based on stochastic subspace realization techniques capable of both (1) identifying the underlying black-box structure thereby enabling the extraction of structural modes that can be used for analysis and modal tracking as well as (2) indicators of condition and possible changes from normal operation.

  2. Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator

    Ronald Phlypo


    Full Text Available To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power. It is shown that the JSSE is able to estimate a component from simulated data that is superior with respect to methodological artefact suppression to those of FastICA, SOBI, pSVD, or JADE/COM1 algorithms used for blind source separation (BSS. Interference and distortion suppression are of comparable order when compared with the above-mentioned methods. Results on patient data demonstrate that the method is able to suppress blinking and saccade artefacts in a fully automated way.

  3. Controllable subspace of edge dynamics in complex networks

    Pang, Shao-Peng; Hao, Fei


    For the edge dynamics in some real networks, it may be neither feasible nor necessary to be fully controlled. An accompanying issue is that, when the external signal is applied to a few nodes or even a single node, how many edges can be controlled? In this paper, for the edge dynamics system, we propose a theoretical framework to determine the controllable subspace and calculate its generic dimension based on the integer linear programming. This framework allows us not only to analyze the control centrality, i.e., the ability of a node to control, but also to uncover the controllable centrality, i.e., the propensity of an edge to be controllable. The simulation results and analytic calculation show that dense and homogeneous networks tend to have larger control centrality of nodes and controllable centrality of edges, but the negatively correlated in- and out-degrees of nodes or edges can reduce the two centrality. The positive correlation between the control centrality of node and its out-degree leads to that the distribution of control centrality, instead of that of controllable centrality, is encoded by the out-degree distribution of networks. Meanwhile, the positive correlation indicates that the nodes with high out-degree tend to play more important roles in control.


    Mario Versaci


    Full Text Available Most of the electromagnetic problems can be stated in terms of an inhomogeneous equation Af = g in which A is a differential, integral or integro-differential operator, g in the exitation source and f is the unknown function to be determined. Methods of Moments (MoM is a procedure to solve the equation above and, by means of an appropriate choice of the Basis/Testing (B/T, the problem can be translated into an equivalent linear system even of bigger dimensions. In this work we investigate on how the performances of the major Krylov’s subspace iterative solvers are affected by different choice of these sets of functions. More specifically, as a test case, we consider the algebric linear system of equations obtained by an electrostatic problem of evaluation of the capacitance and electrostatic charge distribution in a cylindrical conductor of finite length. Results are compared in terms of analytical/computational complexity and speed of convergence by exploiting three leading iterative methods (GMRES, CGS, BibGStab and B/T functions of Pulse/Pulse (P/P and Pulse/Delta (P/D type.

  5. Generalized shift-invariant systems and frames for subspaces

    Christensen, Ole; Eldar, Y.C.


    )(j is an element of J,k is an element of Z) are Bessel sequences, we are interested in expansions [GRAPHICS] Our main result gives an equivalent condition for this to hold in a more general setting than described here, where translation by k is an element of Z(d) is replaced by translation via the action......Let T-k denote translation by k is an element of Z(d). Given countable collections of functions {phi(j)}(j is an element of J), {(phi) over bar (j)}(j is an element of J) subset of L-2(R-d) and assuming that {T(k)phi(j)}(j is an element of J,k is an element of Z)(d) and {T-k(phi) over bar (j)} (d...... of a matrix. As special cases of our result we find conditions for shift-invariant systems, Gabor systems, and wavelet systems to generate a subspace frame with a corresponding dual having the same structure....

  6. Discrete Noether Currents

    Seidl, Gerhart


    We present a simple generalization of Noether's theorem for discrete symmetries in relativistic continuum field theories. We calculate explicitly the conserved current for several discrete spacetime and internal symmetries. In addition, we formulate an analogue of the Ward-Takahashi identity for the Noether current associated with a discrete symmetry.

  7. Absence of parasympathetic reactivation after maximal exercise.

    de Oliveira, Tiago Peçanha; de Alvarenga Mattos, Raphael; da Silva, Rhenan Bartels Ferreira; Rezende, Rafael Andrade; de Lima, Jorge Roberto Perrout


    The ability of the human organism to recover its autonomic balance soon after physical exercise cessation has an important impact on the individual's health status. Although the dynamics of heart rate recovery after maximal exercise has been studied, little is known about heart rate variability after this type of exercise. The aim of this study is to analyse the dynamics of heart rate and heart rate variability recovery after maximal exercise in healthy young men. Fifteen healthy male subjects (21·7 ± 3·4 years; 24·0 ± 2·1 kg m(-2) ) participated in the study. The experimental protocol consisted of an incremental maximal exercise test on a cycle ergometer, until maximal voluntary exhaustion. After the test, recovery R-R intervals were recorded for 5 min. From the absolute differences between peak heart rate values and the heart rate values at 1 and 5 min of the recovery, the heart rate recovery was calculated. Postexercise heart rate variability was analysed from calculations of the SDNN and RMSSD indexes, in 30-s windows (SDNN(30s) and RMSSD(30s) ) throughout recovery. One and 5 min after maximal exercise cessation, the heart rate recovered 34·7 (±6·6) and 75·5 (±6·1) bpm, respectively. With regard to HRV recovery, while the SDNN(30s) index had a slight increase, RMSSD(30s) index remained totally suppressed throughout the recovery, suggesting an absence of vagal modulation reactivation and, possibly, a discrete sympathetic withdrawal. Therefore, it is possible that the main mechanism associated with the fall of HR after maximal exercise is sympathetic withdrawal or a vagal tone restoration without vagal modulation recovery. © 2012 The Authors Clinical Physiology and Functional Imaging © 2012 Scandinavian Society of Clinical Physiology and Nuclear Medicine.

  8. Maximally Atomic Languages

    Janusz Brzozowski


    Full Text Available The atoms of a regular language are non-empty intersections of complemented and uncomplemented quotients of the language. Tight upper bounds on the number of atoms of a language and on the quotient complexities of atoms are known. We introduce a new class of regular languages, called the maximally atomic languages, consisting of all languages meeting these bounds. We prove the following result: If L is a regular language of quotient complexity n and G is the subgroup of permutations in the transition semigroup T of the minimal DFA of L, then L is maximally atomic if and only if G is transitive on k-subsets of 1,...,n for 0 <= k <= n and T contains a transformation of rank n-1.

  9. Guinea pig maximization test

    Andersen, Klaus Ejner


    Guinea pig maximization tests (GPMT) with chlorocresol were performed to ascertain whether the sensitization rate was affected by minor changes in the Freund's complete adjuvant (FCA) emulsion used. Three types of emulsion were evaluated: the oil phase was mixed with propylene glycol, saline with...... to the saline/oil emulsion. Placing of the challenge patches affected the response, as simultaneous chlorocresol challenge on the flank located 2 cm closer to the abdomen than the usual challenge site gave decreased reactions....

  10. Quantum-Inspired Maximizer

    Zak, Michail


    A report discusses an algorithm for a new kind of dynamics based on a quantum- classical hybrid-quantum-inspired maximizer. The model is represented by a modified Madelung equation in which the quantum potential is replaced by different, specially chosen 'computational' potential. As a result, the dynamics attains both quantum and classical properties: it preserves superposition and entanglement of random solutions, while allowing one to measure its state variables, using classical methods. Such optimal combination of characteristics is a perfect match for quantum-inspired computing. As an application, an algorithm for global maximum of an arbitrary integrable function is proposed. The idea of the proposed algorithm is very simple: based upon the Quantum-inspired Maximizer (QIM), introduce a positive function to be maximized as the probability density to which the solution is attracted. Then the larger value of this function will have the higher probability to appear. Special attention is paid to simulation of integer programming and NP-complete problems. It is demonstrated that the problem of global maximum of an integrable function can be found in polynomial time by using the proposed quantum- classical hybrid. The result is extended to a constrained maximum with applications to integer programming and TSP (Traveling Salesman Problem).

  11. Domain decomposed preconditioners with Krylov subspace methods as subdomain solvers

    Pernice, M. [Univ. of Utah, Salt Lake City, UT (United States)


    Domain decomposed preconditioners for nonsymmetric partial differential equations typically require the solution of problems on the subdomains. Most implementations employ exact solvers to obtain these solutions. Consequently work and storage requirements for the subdomain problems grow rapidly with the size of the subdomain problems. Subdomain solves constitute the single largest computational cost of a domain decomposed preconditioner, and improving the efficiency of this phase of the computation will have a significant impact on the performance of the overall method. The small local memory available on the nodes of most message-passing multicomputers motivates consideration of the use of an iterative method for solving subdomain problems. For large-scale systems of equations that are derived from three-dimensional problems, memory considerations alone may dictate the need for using iterative methods for the subdomain problems. In addition to reduced storage requirements, use of an iterative solver on the subdomains allows flexibility in specifying the accuracy of the subdomain solutions. Substantial savings in solution time is possible if the quality of the domain decomposed preconditioner is not degraded too much by relaxing the accuracy of the subdomain solutions. While some work in this direction has been conducted for symmetric problems, similar studies for nonsymmetric problems appear not to have been pursued. This work represents a first step in this direction, and explores the effectiveness of performing subdomain solves using several transpose-free Krylov subspace methods, GMRES, transpose-free QMR, CGS, and a smoothed version of CGS. Depending on the difficulty of the subdomain problem and the convergence tolerance used, a reduction in solution time is possible in addition to the reduced memory requirements. The domain decomposed preconditioner is a Schur complement method in which the interface operators are approximated using interface probing.

  12. Implementation of Preconditioned Krylov Subspace Method in MATRA Code for Whole Core Analysis of SMART

    Kwon, Hyuk; Kim, S. J.; Park, J. P.; Hwang, D. H. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)


    Krylov subspace method was implemented to perform the efficient whole core calculation of SMART with pin by pin subchannel model without lumping channel. The SMART core consisted of 57 fuel assemblies of 17 by 17 arrays with 264 fuel rods and 25 guide tubes and there are total 15,048 fuel rods and 16,780 subchannels. Restarted GMRES and BiCGStab methods are selected among Krylov subspace methods. For the purpose of verifying the implementation of Krylov method, whole core problem is considered under the normal operating condition. In this problem, solving a linear system Aχ = b is considered when A is nearly symmetric and when the system is preconditioned with incomplete LU factorization(ILU). The preconditioner using incomplete LU factorization are among the most effective preconditioners for solving general large, sparse linear systems arising from practical engineering problem. The Krylov subspace method is expected to improve the calculation effectiveness of MATRA code rather than direct method and stationary iteration method such as Gauss elimination and SOR. The present study describes the implementation of Krylov subspace methods with ILU into MATRA code. In this paper, we explore an improved performance of MATRA code for the SMART whole core problems by of Krylov subspace method. For this purpose, two preconditioned Krylov subspace methods, GMRES and BiCGStab, are implemented into the subchannel code MATRA. A typical ILU method is used as the preconditioner. Numerical problems examined in this study indicate that the Krylov subspace method shows the outstanding improvements in the calculation speed and easy convergence.

  13. Conjunctive patches subspace learning with side information for collaborative image retrieval.

    Zhang, Lining; Wang, Lipo; Lin, Weisi


    Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.

  14. Social group utility maximization

    Gong, Xiaowen; Yang, Lei; Zhang, Junshan


    This SpringerBrief explains how to leverage mobile users' social relationships to improve the interactions of mobile devices in mobile networks. It develops a social group utility maximization (SGUM) framework that captures diverse social ties of mobile users and diverse physical coupling of mobile devices. Key topics include random access control, power control, spectrum access, and location privacy.This brief also investigates SGUM-based power control game and random access control game, for which it establishes the socially-aware Nash equilibrium (SNE). It then examines the critical SGUM-b

  15. Maximizing Modularity is hard

    Brandes, U; Gaertler, M; Goerke, R; Hoefer, M; Nikoloski, Z; Wagner, D


    Several algorithms have been proposed to compute partitions of networks into communities that score high on a graph clustering index called modularity. While publications on these algorithms typically contain experimental evaluations to emphasize the plausibility of results, none of these algorithms has been shown to actually compute optimal partitions. We here settle the unknown complexity status of modularity maximization by showing that the corresponding decision version is NP-complete in the strong sense. As a consequence, any efficient, i.e. polynomial-time, algorithm is only heuristic and yields suboptimal partitions on many instances.

  16. Maximizing without difficulty: A modified maximizing scale and its correlates

    Linda Lai


    This article presents several studies that replicate and extend previous research on maximizing. A modified scale for measuring individual maximizing tendency is introduced. The scale has adequate psychometric properties and reflects maximizers' aspirations for high standards and their preference for extensive alternative search, but not the decision difficulty aspect included in several previous studies. Based on this scale, maximizing is positively correlated with optimism, need for cogniti...

  17. Single-shot realization of nonadiabatic holonomic quantum gates in decoherence-free subspaces

    Zhao, P. Z.; Xu, G. F.; Ding, Q. M.; Sjöqvist, Erik; Tong, D. M.


    Nonadiabatic holonomic quantum computation in decoherence-free subspaces has attracted increasing attention recently, as it allows for high-speed implementation and combines both the robustness of holonomic gates and the coherence stabilization of decoherence-free subspaces. Since the first protocol of nonadiabatic holonomic quantum computation in decoherence-free subspaces, a number of schemes for its physical implementation have been put forward. However, all previous schemes require two noncommuting gates to realize an arbitrary one-qubit gate, which doubles the exposure time of gates to error sources as well as the resource expenditure. In this paper, we propose an alternative protocol for nonadiabatic holonomic quantum computation in decoherence-free subspaces, in which an arbitrary one-qubit gate in decoherence-free subspaces is realized by a single-shot implementation. The present protocol not only maintains the merits of the original protocol but also avoids the extra work of combining two gates to implement an arbitrary one-qubit gate and thereby reduces the exposure time to various error sources.

  18. A Kernel—based Nonlinear Subspace Projection Method for Dimensionality Reduction of Hyperspectral Image Data

    GUYanfeng; ZHANGYe; QUANTaifan


    A challenging problem in using hyper-spectral data is to eliminate redundancy and preserve useful spectral information for applications. In this pa-per, a kernel-based nonlinear subspace projection (KNSP)method is proposed for feature extraction and dimension-ality reduction in hyperspectral images. The proposed method includes three key steps: subspace partition of hyperspectral data, feature extraction using kernel-based principal component analysis (KPCA) and feature selec-tion based on class separability in the subspaces. Accord-ing to the strong correlation between neighboring bands,the whole data space is partitioned to requested subspaces.In each subspace, the KPCA method is used to effectively extract spectral feature and eliminate redundancies. A criterion function based on class discrimination and sepa-rability is used for the transformed feature selection. For the purpose of testifying its effectiveness, the proposed new method is compared with the classical principal component analysis (PCA) and segmented principal component trans-formation (SPCT). A hyperspectral image classification is performed on AVIRIS data. which have 224 svectral bands.Experimental results show that KNSP is very effective for feature extraction and dimensionality reduction of hyper-spectral data and provides significant improvement over classical PCA and current SPCT technique.

  19. Statistical discrete geometry

    Ariwahjoedi, Seramika; Kosasih, Jusak Sali; Rovelli, Carlo; Zen, Freddy Permana


    Following our earlier work, we construct statistical discrete geometry by applying statistical mechanics to discrete (Regge) gravity. We propose a coarse-graining method for discrete geometry under the assumptions of atomism and background independence. To maintain these assumptions, restrictions are given to the theory by introducing cut-offs, both in ultraviolet and infrared regime. Having a well-defined statistical picture of discrete Regge geometry, we take the infinite degrees of freedom (large n) limit. We argue that the correct limit consistent with the restrictions and the background independence concept is not the continuum limit of statistical mechanics, but the thermodynamical limit.

  20. HEMI: Hyperedge Majority Influence Maximization

    Gangal, Varun; Narayanam, Ramasuri


    In this work, we consider the problem of influence maximization on a hypergraph. We first extend the Independent Cascade (IC) model to hypergraphs, and prove that the traditional influence maximization problem remains submodular. We then present a variant of the influence maximization problem (HEMI) where one seeks to maximize the number of hyperedges, a majority of whose nodes are influenced. We prove that HEMI is non-submodular under the diffusion model proposed.

  1. Guinea pig maximization test

    Andersen, Klaus Ejner


    Guinea pig maximization tests (GPMT) with chlorocresol were performed to ascertain whether the sensitization rate was affected by minor changes in the Freund's complete adjuvant (FCA) emulsion used. Three types of emulsion were evaluated: the oil phase was mixed with propylene glycol, saline...... with 30% (v/v) ethanol or saline, respectively. Relative viscosity was used as one measure of physical properties of the emulsion. Higher degrees of sensitization (but not rates) were obtained at the 48 h challenge reading with the oil/propylene glycol and oil/saline + ethanol emulsions compared...... to the saline/oil emulsion. Placing of the challenge patches affected the response, as simultaneous chlorocresol challenge on the flank located 2 cm closer to the abdomen than the usual challenge site gave decreased reactions....

  2. Discrete mathematics, discrete physics and numerical methods

    Felice Iavernaro; Donato Trigiante


    Discrete mathematics has been neglected for a long time. It has been put in the shade by the striking success of continuous mathematics in the last two centuries, mainly because continuous models in physics proved very reliable, but also because of the greater difficulty in dealing with it. This perspective has been rapidly changing in the last years owing to the needs of the numerical analysis and, more recently, of the so called discrete physics. In this paper, starting from some sentences o...

  3. Efficient Discretization of Stochastic Integrals

    Fukasawa, Masaaki


    Sharp asymptotic lower bounds of the expected quadratic variation of discretization error in stochastic integration are given. The theory relies on inequalities for the kurtosis and skewness of a general random variable which are themselves seemingly new. Asymptotically efficient schemes which attain the lower bounds are constructed explicitly. The result is directly applicable to practical hedging problem in mathematical finance; it gives an asymptotically optimal way to choose rebalancing dates and portofolios with respect to transaction costs. The asymptotically efficient strategies in fact reflect the structure of transaction costs. In particular a specific biased rebalancing scheme is shown to be superior to unbiased schemes if transaction costs follow a convex model. The problem is discussed also in terms of the exponential utility maximization.

  4. Quantum evolution by discrete measurements

    Roa, L [Center for Quantum Optics and Quantum Information, Departamento de Fisica, Universidad de Concepcion, Casilla 160-C, Concepcion (Chile); Guevara, M L Ladron de [Departamento de Fisica, Universidad Catolica del Norte, Casilla 1280, Antofagasta (Chile); Delgado, A [Center for Quantum Optics and Quantum Information, Departamento de Fisica, Universidad de Concepcion, Casilla 160-C, Concepcion (Chile); Olivares-RenterIa, G [Center for Quantum Optics and Quantum Information, Departamento de Fisica, Universidad de Concepcion, Casilla 160-C, Concepcion (Chile); Klimov, A B [Departamento de Fisica, Universidad de Guadalajara, Revolucion 1500, 44420 Guadalajara, Jalisco (Mexico)


    In this article we review two ways of driving a quantum system to a known pure state via a sequence discrete of von Neumann measurements. The first of them assumes that the initial state of the system is unknown, and the evolution is attained only with the help of two non-commuting observables. For this method, the overall success probability is maximized when the eigentstates of the involved observables constitute mutually unbiased bases. The second method assumes the initial state is known and it uses N observables which are consecutively measured to make the state of the system approach the target state. The probability of success of this procedure converges to 1 as the number of observables increases.

  5. Lepton mixing and discrete symmetries

    Hernandez, D.; Smirnov, A. Yu.


    The pattern of lepton mixing can emerge from breaking a flavor symmetry in different ways in the neutrino and charged lepton Yukawa sectors. In this framework, we derive the model-independent conditions imposed on the mixing matrix by the structure of discrete groups of the von Dyck type which include A4, S4, and A5. We show that, in general, these conditions lead to at least two equations for the mixing parameters (angles and CP phase δ). These constraints, which correspond to unbroken residual symmetries, are consistent with nonzero 13 mixing and deviations from maximal 2-3 mixing. For the simplest case, which leads to an S4 model and reproduces the allowed values of the mixing angles, we predict δ=(90°-120°).

  6. Nonadiabatic geometric quantum computation in decoherence-free subspaces based on unconventional geometric phases

    Zhao, P. Z.; Xu, G. F.; Tong, D. M.


    Nonadiabatic geometric quantum computation in decoherence-free subspaces has received increasing attention due to the merits of its high-speed implementation and robustness against both control errors and decoherence. However, all the previous schemes in this direction have been based on the conventional geometric phases, of which the dynamical phases need to be removed. In this paper, we put forward a scheme of nonadiabatic geometric quantum computation in decoherence-free subspaces based on unconventional geometric phases, of which the dynamical phases do not need to be removed. Specifically, by using three physical qubits undergoing collective dephasing to encode one logical qubit, we realize a universal set of geometric gates nonadiabatically and unconventionally. Our scheme not only maintains all the merits of nonadiabatic geometric quantum computation in decoherence-free subspaces, but also avoids the additional operations required in the conventional schemes to cancel the dynamical phases.

  7. Subspace electrode selection methodology for EEG multiple source localization error reduction due to uncertain conductivity values.

    Crevecoeur, Guillaume; Yitembe, Bertrand; Dupre, Luc; Van Keer, Roger


    This paper proposes a modification of the subspace correlation cost function and the Recursively Applied and Projected Multiple Signal Classification (RAP-MUSIC) method for electroencephalography (EEG) source analysis in epilepsy. This enables to reconstruct neural source locations and orientations that are less degraded due to the uncertain knowledge of the head conductivity values. An extended linear forward model is used in the subspace correlation cost function that incorporates the sensitivity of the EEG potentials to the uncertain conductivity value parameter. More specifically, the principal vector of the subspace correlation function is used to provide relevant information for solving the EEG inverse problems. A simulation study is carried out on a simplified spherical head model with uncertain skull to soft tissue conductivity ratio. Results show an improvement in the reconstruction accuracy of source parameters compared to traditional methodology, when using conductivity ratio values that are different from the actual conductivity ratio.

  8. Estimation of direction of arrival of a moving target using subspace based approaches

    Ghosh, Ripul; Das, Utpal; Akula, Aparna; Kumar, Satish; Sardana, H. K.


    In this work, array processing techniques based on subspace decomposition of signal have been evaluated for estimation of direction of arrival of moving targets using acoustic signatures. Three subspace based approaches - Incoherent Wideband Multiple Signal Classification (IWM), Least Square-Estimation of Signal Parameters via Rotation Invariance Techniques (LS-ESPRIT) and Total Least Square- ESPIRIT (TLS-ESPRIT) are considered. Their performance is compared with conventional time delay estimation (TDE) approaches such as Generalized Cross Correlation (GCC) and Average Square Difference Function (ASDF). Performance evaluation has been conducted on experimentally generated data consisting of acoustic signatures of four different types of civilian vehicles moving in defined geometrical trajectories. Mean absolute error and standard deviation of the DOA estimates w.r.t. ground truth are used as performance evaluation metrics. Lower statistical values of mean error confirm the superiority of subspace based approaches over TDE based techniques. Amongst the compared methods, LS-ESPRIT indicated better performance.

  9. A real-time cardiac surface tracking system using Subspace Clustering.

    Singh, Vimal; Tewfik, Ahmed H; Gowreesunker, B


    Catheter based radio frequency ablation of atrial fibrillation requires real-time 3D tracking of cardiac surfaces with sub-millimeter accuracy. To best of our knowledge, there are no commercial or non-commercial systems capable to do so. In this paper, a system for high-accuracy 3D tracking of cardiac surfaces in real-time is proposed and results applied to a real patient dataset are presented. Proposed system uses Subspace Clustering algorithm to identify the potential deformation subspaces for cardiac surfaces during the training phase from pre-operative MRI scan based training set. In Tracking phase, using low-density outer cardiac surface samples, active deformation subspace is identified and complete inner & outer cardiac surfaces are reconstructed in real-time under a least squares formulation.

  10. Rational time-frequency multi-window subspace Gabor frames and their Gabor duals

    ZHANG Yan; LI YunZhang


    This paper addresses the theory of multi-window subspace Gabor frame with rational time-frequency parameter products.With the help of a suitable Zak transform matrix,we characterize multi-window subspace Gabor frames,Riesz bases,orthonormal bases and the uniqueness of Gabor duals of type I and type II.Using these characterizations we obtain a class of multi-window subspace Gabor frames,Riesz bases,orthonormal bases,and at the same time we derive an explicit expression of their Gabor duals of type I and type II.As an application of the above results,we obtain characterizations of multi-window Gabor frames,Riesz bases and orthonormal bases for L2(R),and derive a parametric expression of Gabor duals for multi-window Gabor frames in L2(R).

  11. Maximal T-spaces of the free associative algebra over a finite field

    Bekh-Ochir, Chuluun


    In earlier work, it was established that for any finite field k, the free associative k-algebra on one generator x, denoted by k[x]_0, had infinitely many maximal T-spaces, but exactly two maximal $ideals (each of which is a maximal T-space). However, aside from these two T-ideals, no examples of maximal T-spaces of k[c]_0 have been identified. This paper presents, for each finite field k, an infinite sequence of proper T-spaces of k[x]_0 (no one of which is a T-ideal), each of finite codimension, and for each one, both a linear basis for the T-space itself and a linear basis for a complementary linear subspace are provided. Morever, it is proven that the first T-space in the sequence is a maximal T-space of k[x]_0, thereby providing the first example of a maximal T-space of k[x]_0 that is not a maximal T-ideal.

  12. Lattice QCD computations: Recent progress with modern Krylov subspace methods

    Frommer, A. [Bergische Universitaet GH Wuppertal (Germany)


    Quantum chromodynamics (QCD) is the fundamental theory of the strong interaction of matter. In order to compare the theory with results from experimental physics, the theory has to be reformulated as a discrete problem of lattice gauge theory using stochastic simulations. The computational challenge consists in solving several hundreds of very large linear systems with several right hand sides. A considerable part of the world`s supercomputer time is spent in such QCD calculations. This paper presents results on solving systems for the Wilson fermions. Recent progress is reviewed on algorithms obtained in cooperation with partners from theoretical physics.

  13. Finite Discrete Gabor Analysis

    Søndergaard, Peter Lempel


    on the real line to be well approximated by finite and discrete Gabor frames. This method of approximation is especially attractive because efficient numerical methods exists for doing computations with finite, discrete Gabor systems. This thesis presents new algorithms for the efficient computation of finite...

  14. Discrete Mathematics Re "Tooled."

    Grassl, Richard M.; Mingus, Tabitha T. Y.


    Indicates the importance of teaching discrete mathematics. Describes how the use of technology can enhance the teaching and learning of discrete mathematics. Explorations using Excel, Derive, and the TI-92 proved how preservice and inservice teachers experienced a new dimension in problem solving and discovery. (ASK)

  15. Discrete Quantum Mechanics

    Chang, Lay Nam; Minic, Djordje; Takeuchi, Tatsu


    We construct a discrete quantum mechanics using a vector space over the Galois field GF(q). We find that the correlations in our model do not violate the Clauser-Horne-Shimony-Holt (CHSH) version of Bell's inequality, despite the fact that the predictions of this discrete quantum mechanics cannot be reproduced with any hidden variable theory.

  16. Robust subspace estimation using low-rank optimization theory and applications

    Oreifej, Omar


    Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book,?the authors?discuss fundame

  17. Krylov subspace methods for the solution of large systems of ODE's

    Thomsen, Per Grove; Bjurstrøm, Nils Henrik


    In Air Pollution Modelling large systems of ODE's arise. Solving such systems may be done efficientliy by Semi Implicit Runge-Kutta methods. The internal stages may be solved using Krylov subspace methods. The efficiency of this approach is investigated and verified.......In Air Pollution Modelling large systems of ODE's arise. Solving such systems may be done efficientliy by Semi Implicit Runge-Kutta methods. The internal stages may be solved using Krylov subspace methods. The efficiency of this approach is investigated and verified....

  18. The Many Faces of the Subspace Theorem (after Adamczewski, Bugeaud, Corvaja, Zannier...)

    Bilu, Yuri


    Séminaire Bourbaki, Exposé 967, 59ème année (2006-2007); Astérisque 317 (2008), 1-38.; During the last decade the Subspace Theorem found several quite unexpected applications, mainly in the Diophantine Analysis and in the Transcendence Theory. Among the great variety of spectacular results, I have chosen several which are technically simpler and which allow one to appreciate how miraculously does the Subspace Theorem emerge in numerous situations, implying beautiful solutions to difficult pro...

  19. Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

    解翔; 侍洪波


    For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

  20. Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

    Hansen, Per Christian; Jensen, Søren Holdt


    We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV and ULLIV). In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filter interpretations. The algorithms are illustrated...... with working Matlab code and applications in speech processing....


    Widya Hanum Sari Pertiwi


    Full Text Available This study was qualitative research action that focuses to find out the flouting of Gricean maxims and the functions of the flouting in the tales which are included in collection of children literature entitled My Giant Treasury of Stories and Rhymes. The objective of the study is generally to identify the violation of maxims of quantity, quality, relevance, and manner in the data sources and also to analyze the use of the flouting in the tales which are included in the book. Qualitative design using categorizing strategies, specifically coding strategy, was applied. Thus, the researcher as the instrument in this investigation was selecting the tales, reading them, and gathering every item which reflects the violation of Gricean maxims based on some conditions of flouting maxims. On the basis of the data analysis, it was found that the some utterances in the tales, both narration and conversation, flouting the four maxims of conversation, namely maxim of quality, maxim of quantity, maxim of relevance, and maxim of manner. The researcher has also found that the flouting of maxims has one basic function that is to encourage the readers’ imagination toward the tales. This one basic function is developed by six others functions: (1 generating specific situation, (2 developing the plot, (3 enlivening the characters’ utterance, (4 implicating message, (5 indirectly characterizing characters, and (6 creating ambiguous setting. Keywords: children literature, tales, flouting maxims

  2. Discrete Control Systems

    Lee, Taeyoung; McClamroch, N Harris


    Discrete control systems, as considered here, refer to the control theory of discrete-time Lagrangian or Hamiltonian systems. These discrete-time models are based on a discrete variational principle, and are part of the broader field of geometric integration. Geometric integrators are numerical integration methods that preserve geometric properties of continuous systems, such as conservation of the symplectic form, momentum, and energy. They also guarantee that the discrete flow remains on the manifold on which the continuous system evolves, an important property in the case of rigid-body dynamics. In nonlinear control, one typically relies on differential geometric and dynamical systems techniques to prove properties such as stability, controllability, and optimality. More generally, the geometric structure of such systems plays a critical role in the nonlinear analysis of the corresponding control problems. Despite the critical role of geometry and mechanics in the analysis of nonlinear control systems, non...

  3. Discrete Element Modeling

    Morris, J; Johnson, S


    The Distinct Element Method (also frequently referred to as the Discrete Element Method) (DEM) is a Lagrangian numerical technique where the computational domain consists of discrete solid elements which interact via compliant contacts. This can be contrasted with Finite Element Methods where the computational domain is assumed to represent a continuum (although many modern implementations of the FEM can accommodate some Distinct Element capabilities). Often the terms Discrete Element Method and Distinct Element Method are used interchangeably in the literature, although Cundall and Hart (1992) suggested that Discrete Element Methods should be a more inclusive term covering Distinct Element Methods, Displacement Discontinuity Analysis and Modal Methods. In this work, DEM specifically refers to the Distinct Element Method, where the discrete elements interact via compliant contacts, in contrast with Displacement Discontinuity Analysis where the contacts are rigid and all compliance is taken up by the adjacent intact material.

  4. Discrete control systems

    Okuyama, Yoshifumi


    Discrete Control Systems establishes a basis for the analysis and design of discretized/quantized control systemsfor continuous physical systems. Beginning with the necessary mathematical foundations and system-model descriptions, the text moves on to derive a robust stability condition. To keep a practical perspective on the uncertain physical systems considered, most of the methods treated are carried out in the frequency domain. As part of the design procedure, modified Nyquist–Hall and Nichols diagrams are presented and discretized proportional–integral–derivative control schemes are reconsidered. Schemes for model-reference feedback and discrete-type observers are proposed. Although single-loop feedback systems form the core of the text, some consideration is given to multiple loops and nonlinearities. The robust control performance and stability of interval systems (with multiple uncertainties) are outlined. Finally, the monograph describes the relationship between feedback-control and discrete ev...

  5. Continuity in Discrete Sets

    Burgin, Mark


    Continuous models used in physics and other areas of mathematics applications become discrete when they are computerized, e.g., utilized for computations. Besides, computers are controlling processes in discrete spaces, such as films and television programs. At the same time, continuous models that are in the background of discrete representations use mathematical technology developed for continuous media. The most important example of such a technology is calculus, which is so useful in physics and other sciences. The main goal of this paper is to synthesize continuous features and powerful technology of the classical calculus with the discrete approach of numerical mathematics and computational physics. To do this, we further develop the theory of fuzzy continuous functions and apply this theory to functions defined on discrete sets. The main interest is the classical Intermediate Value theorem. Although the result of this theorem is completely based on continuity, utilization of a relaxed version of contin...

  6. Maximal equilateral sets

    Swanepoel, Konrad J


    A subset of a normed space X is called equilateral if the distance between any two points is the same. Let m(X) be the smallest possible size of an equilateral subset of X maximal with respect to inclusion. We first observe that Petty's construction of a d-dimensional X of any finite dimension d >= 4 with m(X)=4 can be generalised to show that m(X\\oplus_1\\R)=4 for any X of dimension at least 2 which has a smooth point on its unit sphere. By a construction involving Hadamard matrices we then show that both m(\\ell_p) and m(\\ell_p^d) are finite and bounded above by a function of p, for all 1 1 such that m(X) <= d+1 for all d-dimensional X with Banach-Mazur distance less than c from \\ell_p^d. Using Brouwer's fixed-point theorem we show that m(X) <= d+1 for all d-\\dimensional X with Banach-Mazur distance less than 3/2 from \\ell_\\infty^d. A graph-theoretical argument furthermore shows that m(\\ell_\\infty^d)=d+1. The above results lead us to conjecture that m(X) <= 1+\\dim X.

  7. Unified Maximally Natural Supersymmetry

    Huang, Junwu


    Maximally Natural Supersymmetry, an unusual weak-scale supersymmetric extension of the Standard Model based upon the inherently higher-dimensional mechanism of Scherk-Schwarz supersymmetry breaking (SSSB), possesses remarkably good fine tuning given present LHC limits. Here we construct a version with precision $SU(2)_{\\rm L} \\times U(1)_{\\rm Y} $ unification: $\\sin^2 \\theta_W(M_Z) \\simeq 0.231$ is predicted to $\\pm 2\\%$ by unifying $SU(2)_{\\rm L} \\times U(1)_{\\rm Y} $ into a 5D $SU(3)_{\\rm EW}$ theory at a Kaluza-Klein scale of $1/R_5 \\sim 4.4\\,{\\rm TeV}$, where SSSB is simultaneously realised. Full unification with $SU(3)_{\\rm C}$ is accommodated by extending the 5D theory to a $N=4$ supersymmetric $SU(6)$ gauge theory on a 6D rectangular orbifold at $1/R_6 \\sim 40 \\,{\\rm TeV}$. TeV-scale states beyond the SM include exotic charged fermions implied by $SU(3)_{\\rm EW}$ with masses lighter than $\\sim 1.2\\,{\\rm TeV}$, and squarks in the mass range $1.4\\,{\\rm TeV} - 2.3\\,{\\rm TeV}$, providing distinct signature...

  8. A Survey on Sparse Subspace Clustering%稀疏子空间聚类综述

    王卫卫; 李小平; 冯象初; 王斯琪


    Sparse subspace clustering (SSC) is a newly developed spectral clustering-based framework for data clustering. High-dimensional data usually lie in a union of several low-dimensional subspaces, which allows sparse representation of high-dimensional data with an appropriate dictionary. Sparse subspace clustering methods pursue a sparse representation of high-dimensional data and use it to build the affinity matrix. The subspace clustering result of the data is finally obtained by means of spectral clustering. The key to sparse subspace clustering is to design a good representation model which can reveal the real subspace structure of high-dimensional data. More importantly, the obtained representation coefficient and the affinity matrix are more beneficial to accurate subspace clustering. Sparse subspace clustering has been successfully applied to different research fields, including machine learning, computer vision, image processing, system identification and others, but there is still a vast space to develop. In this paper, the fundamental models, algorithms and applications of sparse subspace clustering are reviewed in detail. Limitations existing in available methods are analyzed. Problems for further research on sparse subspace clustering are discussed.%稀疏子空间聚类(Sparse subspace clustering, SSC)是一种基于谱聚类的数据聚类框架。高维数据通常分布于若干个低维子空间的并上,因此高维数据在适当字典下的表示具有稀疏性。稀疏子空间聚类利用高维数据的稀疏表示系数构造相似度矩阵,然后利用谱聚类方法得到数据的子空间聚类结果。其核心是设计能够揭示高维数据真实子空间结构的表示模型,使得到的表示系数及由此构造的相似度矩阵有助于精确的子空间聚类。稀疏子空间聚类在机器学习、计算机视觉、图像处理和模式识别等领域已经得到了广泛的研究和应用,但仍有很大的发展空间。本文

  9. Torus Bifurcation Under Discretization

    邹永魁; 黄明游


    Parameterized dynamical systems with a simple zero eigenvalue and a couple of purely imaginary eigenvalues are considered. It is proved that this type of eigen-structure leads to torns bifurcation under certain nondegenerate conditions. We show that the discrete systems, obtained by discretizing the ODEs using symmetric, eigen-structure preserving schemes, inherit the similar torus bifurcation properties. Fredholm theory in Banach spaces is applied to obtain the global torns bifurcation. Our results complement those on the study of discretization effects of global bifurcation.

  10. Discrete density of states

    Aydin, Alhun; Sisman, Altug


    By considering the quantum-mechanically minimum allowable energy interval, we exactly count number of states (NOS) and introduce discrete density of states (DOS) concept for a particle in a box for various dimensions. Expressions for bounded and unbounded continua are analytically recovered from discrete ones. Even though substantial fluctuations prevail in discrete DOS, they're almost completely flattened out after summation or integration operation. It's seen that relative errors of analytical expressions of bounded/unbounded continua rapidly decrease for high NOS values (weak confinement or high energy conditions), while the proposed analytical expressions based on Weyl's conjecture always preserve their lower error characteristic.

  11. Kernel based subspace projection of near infrared hyperspectral images of maize kernels

    Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben;


    In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...

  12. A block Krylov subspace time-exact solution method for linear ordinary differential equation systems

    Botchev, M.A.


    We propose a time-exact Krylov-subspace-based method for solving linear ordinary differential equation systems of the form $y'=-Ay+g(t)$ and $y"=-Ay+g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial approximation of th

  13. A block Krylov subspace time-exact solution method for linear ODE systems

    Botchev, M.A.


    We propose a time-exact Krylov-subspace-based method for solving linear ODE (ordinary differential equation) systems of the form $y'=-Ay + g(t)$ and $y''=-Ay + g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial approxim

  14. Fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections

    Hong Liang


    Fuzzy ordered linear spaces, Riesz spaces, fuzzy Archimedean spaces and $\\sigma$-complete fuzzy Riesz spaces were defined and studied in several works. Following the efforts along this line, we define fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections and establish their fundamental properties.

  15. Recursive subspace identification of linear and non-linear Wiener state-space models

    Lovera, Marco; Gustafsson, Tony; Verhaegen, M.H.G.


    The problem of MIMO recursive identification is analyzed within the framework of subspace model identification (SMI) and the use of recent signal processing algorithms for the recursive update of the singular value decomposition (SVD) is proposed. To accommodate for arbitrary correlation of the dist

  16. Active constraints selection based semi-supervised dimensionality in ensemble subspaces

    Jie Zeng; Wei Nie; Yong Zhang


    Semi-supervised dimensionality reduction (SSDR) has attracted an increasing amount of attention in this big-data era. Many algorithms have been developed with a smal number of pairwise constraints to achieve performances comparable to those of ful y supervised methods. However, one chal enging problem with semi-supervised approaches is the appropriate choice of the constraint set, including the cardinality and the composition of the constraint set, which to a large extent, affects the performance of the resulting algorithm. In this work, we address the problem by incorporating ensemble subspace and active learning into dimen-sionality reduction and propose a new algorithm, termed as global and local scatter based SSDR with active pairwise constraints selection in ensemble subspaces (SSGL-ESA). Unlike traditional methods that select the supervised information in one subspace, we pick up pairwise constraints in ensemble subspace, where a novel active learning algorithm is designed with both exploration and filtering to generate informative pairwise constraints. The auto-matic constraint selection approach proposed in this paper can be generalized to be used with al constraint-based semi-supervised learning algorithms. Comparative experiments are conducted on two face database and the results validate the effectiveness of the proposed method.

  17. Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

    Hansen, Per Christian; Jensen, Søren Holdt


    We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using bo...... with working Matlab code and applications in speech processing....

  18. Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

    Hansen, Per Christian; Jensen, Søren Holdt

    We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using bo...... with working Matlab code and applications in speech processing....

  19. Kernel based subspace projection of near infrared hyperspectral images of maize kernels

    Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben


    In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...

  20. Krylov subspace method for evaluating the self-energy matrices in electron transport calculations

    Sørensen, Hans Henrik Brandenborg; Hansen, Per Christian; Petersen, D. E.;


    We present a Krylov subspace method for evaluating the self-energy matrices used in the Green's function formulation of electron transport in nanoscale devices. A procedure based on the Arnoldi method is employed to obtain solutions of the quadratic eigenvalue problem associated with the infinite...

  1. Practical Low Data-Complexity Subspace-Trail Cryptanalysis of Round-Reduced PRINCE

    Grassi, Lorenzo; Rechberger, Christian


    of a 2.5 rounds subspace trail of PRINCE, we present several (truncated differential) attacks up to 6 rounds of PRINCE. This includes a very practical attack with the lowest data complexity of only 8 plaintexts for 4 rounds, which co-won the final round of the PRINCE challenge in the 4-round chosen...

  2. Consistency analysis of subspace identification methods based on a linear regression approach

    Knudsen, Torben


    In the literature results can be found which claim consistency for the subspace method under certain quite weak assumptions. Unfortunately, a new result gives a counter example showing inconsistency under these assumptions and then gives new more strict sufficient assumptions which however does n...

  3. Incomplete Phase Space Reconstruction Method Based on Subspace Adaptive Evolution Approximation

    Tai-fu Li


    Full Text Available The chaotic time series can be expanded to the multidimensional space by phase space reconstruction, in order to reconstruct the dynamic characteristics of the original system. It is difficult to obtain complete phase space for chaotic time series, as a result of the inconsistency of phase space reconstruction. This paper presents an idea of subspace approximation. The chaotic time series prediction based on the phase space reconstruction can be considered as the subspace approximation problem in different neighborhood at different time. The common static neural network approximation is suitable for a trained neighborhood, but it cannot ensure its generalization performance in other untrained neighborhood. The subspace approximation of neural network based on the nonlinear extended Kalman filtering (EKF is a dynamic evolution approximation from one neighborhood to another. Therefore, in view of incomplete phase space, due to the chaos phase space reconstruction, we put forward subspace adaptive evolution approximation method based on nonlinear Kalman filtering. This method is verified by multiple sets of wind speed prediction experiments in Wulong city, and the results demonstrate that it possesses higher chaotic prediction accuracy.

  4. A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery

    Kwon Heesung


    Full Text Available Several linear and nonlinear detection algorithms that are based on spectral matched (subspace filters are compared. Nonlinear (kernel versions of these spectral matched detectors are also given and their performance is compared with linear versions. Several well-known matched detectors such as matched subspace detector, orthogonal subspace detector, spectral matched filter, and adaptive subspace detector are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The expression for each detection algorithm is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. Experimental results based on simulated toy examples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.

  5. A block Krylov subspace time-exact solution method for linear ordinary differential equation systems

    Bochev, Mikhail A.


    We propose a time-exact Krylov-subspace-based method for solving linear ordinary differential equation systems of the form $y'=-Ay+g(t)$ and $y"=-Ay+g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial approximation of

  6. A block Krylov subspace time-exact solution method for linear ODE systems

    Bochev, Mikhail A.

    We propose a time-exact Krylov-subspace-based method for solving linear ODE (ordinary differential equation) systems of the form $y'=-Ay + g(t)$ and $y''=-Ay + g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial

  7. Residual replacement strategies for Krylov subspace iterative methods for the convergence of true residuals

    Vorst, H.A. van der; Ye, Q.


    In this paper, a strategy is proposed for alternative computations of the residual vectors in Krylov subspace methods, which improves the agreement of the computed residuals and the true residuals to the level of O(u)kAkkxk. Building on earlier ideas on residual replacement and on insights in

  8. Experimental Study of Generalized Subspace Filters for the Cocktail Party Situation

    Christensen, Knud Bank; Christensen, Mads Græsbøll; Boldt, Jesper B.


    This paper investigates the potential performance of generalized subspace filters for speech enhancement in cocktail party situations with very poor signal/noise ratio, e.g. down to -15 dB. Performance metrics output signal/noise ratio, signal/ distortion ratio, speech quality rating and speech...

  9. A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan


    In high dimensional databases, traditional full space clustering methods are known to fail due to the curse of dimensionality. Thus, in recent years, subspace clustering and projected clustering approaches were proposed for clustering in high dimensional spaces. As the area is rather young, few c...

  10. Subspace-Based Algorithms for Structural Identification, Damage Detection, and Sensor Data Fusion

    Goursat Maurice


    Full Text Available This paper reports on the theory and practice of covariance-driven output-only and input/output subspace-based identification and detection algorithms. The motivating and investigated application domain is vibration-based structural analysis and health monitoring of mechanical, civil, and aeronautic structures.

  11. Maximal subgroups of finite groups

    S. Srinivasan


    Full Text Available In finite groups maximal subgroups play a very important role. Results in the literature show that if the maximal subgroup has a very small index in the whole group then it influences the structure of the group itself. In this paper we study the case when the index of the maximal subgroups of the groups have a special type of relation with the Fitting subgroup of the group.

  12. Finding Maximal Quasiperiodicities in Strings

    Brodal, Gerth Stølting; Pedersen, Christian N. S.


    of length n in time O(n log n) and space O(n). Our algorithm uses the suffix tree as the fundamental data structure combined with efficient methods for merging and performing multiple searches in search trees. Besides finding all maximal quasiperiodic substrings, our algorithm also marks the nodes......Apostolico and Ehrenfeucht defined the notion of a maximal quasiperiodic substring and gave an algorithm that finds all maximal quasiperiodic substrings in a string of length n in time O(n log2 n). In this paper we give an algorithm that finds all maximal quasiperiodic substrings in a string...

  13. Pearls of Discrete Mathematics

    Erickson, Martin


    Presents methods for solving counting problems and other types of problems that involve discrete structures. This work illustrates the relationship of these structures to algebra, geometry, number theory and combinatorics. It addresses topics such as information and game theories

  14. Discrete fractional calculus

    Goodrich, Christopher


    This text provides the first comprehensive treatment of the discrete fractional calculus. Experienced researchers will find the text useful as a reference for discrete fractional calculus and topics of current interest. Students who are interested in learning about discrete fractional calculus will find this text to provide a useful starting point. Several exercises are offered at the end of each chapter and select answers have been provided at the end of the book. The presentation of the content is designed to give ample flexibility for potential use in a myriad of courses and for independent study. The novel approach taken by the authors includes a simultaneous treatment of the fractional- and integer-order difference calculus (on a variety of time scales, including both the usual forward and backwards difference operators). The reader will acquire a solid foundation in the classical topics of the discrete calculus while being introduced to exciting recent developments, bringing them to the frontiers of the...

  15. Reflection quasilattices and the maximal quasilattice

    Boyle, Latham; Steinhardt, Paul J.


    We introduce the concept of a reflection quasilattice, the quasiperiodic generalization of a Bravais lattice with irreducible reflection symmetry. Among their applications, reflection quasilattices are the reciprocal (i.e., Bragg diffraction) lattices for quasicrystals and quasicrystal tilings, such as Penrose tilings, with irreducible reflection symmetry and discrete scale invariance. In a follow-up paper, we will show that reflection quasilattices can be used to generate tilings in real space with properties analogous to those in Penrose tilings, but with different symmetries and in various dimensions. Here we explain that reflection quasilattices only exist in dimensions two, three, and four, and we prove that there is a unique reflection quasilattice in dimension four: the "maximal reflection quasilattice" in terms of dimensionality and symmetry. Unlike crystallographic Bravais lattices, all reflection quasilattices are invariant under rescaling by certain discrete scale factors. We tabulate the complete set of scale factors for all reflection quasilattices in dimension d >2 , and for all those with quadratic irrational scale factors in d =2 .

  16. The Discrete Wavelet Transform


    focuses on bringing together two separately motivated implementations of the wavelet transform , the algorithm a trous and Mallat’s multiresolution...decomposition. These algorithms are special cases of a single filter bank structure, the discrete wavelet transform , the behavior of which is governed by...nonorthogonal multiresolution algorithm for which the discrete wavelet transform is exact. Moreover, we show that the commonly used Lagrange a trous

  17. Discrete computational structures

    Korfhage, Robert R


    Discrete Computational Structures describes discrete mathematical concepts that are important to computing, covering necessary mathematical fundamentals, computer representation of sets, graph theory, storage minimization, and bandwidth. The book also explains conceptual framework (Gorn trees, searching, subroutines) and directed graphs (flowcharts, critical paths, information network). The text discusses algebra particularly as it applies to concentrates on semigroups, groups, lattices, propositional calculus, including a new tabular method of Boolean function minimization. The text emphasize

  18. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

    Keshtkaran, Mohammad Reza; Yang, Zhi


    Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  19. Improved Detection of Local Earthquakes in the Vienna Basin (Austria), using Subspace Detectors

    Apoloner, Maria-Theresia; Caffagni, Enrico; Bokelmann, Götz


    The Vienna Basin in Eastern Austria is densely populated and highly-developed; it is also a region of low to moderate seismicity, yet the seismological network coverage is relatively sparse. This demands improving our capability of earthquake detection by testing new methods, enlarging the existing local earthquake catalogue. This contributes to imaging tectonic fault zones for better understanding seismic hazard, also through improved earthquake statistics (b-value, magnitude of completeness). Detection of low-magnitude earthquakes or events for which the highest amplitudes slightly exceed the signal-to-noise-ratio (SNR), may be possible by using standard methods like the short-term over long-term average (STA/LTA). However, due to sparse network coverage and high background noise, such a technique may not detect all potentially recoverable events. Yet, earthquakes originating from the same source region and relatively close to each other, should be characterized by similarity in seismic waveforms, at a given station. Therefore, waveform similarity can be exploited by using specific techniques such as correlation-template based (also known as matched filtering) or subspace detection methods (based on the subspace theory). Matching techniques basically require a reference or template event, usually characterized by high waveform coherence in the array receivers, and high SNR, which is cross-correlated with the continuous data. Instead, subspace detection methods overcome in principle the necessity of defining template events as single events, but use a subspace extracted from multiple events. This approach theoretically should be more robust in detecting signals that exhibit a strong variability (e.g. because of source or magnitude). In this study we scan the continuous data recorded in the Vienna Basin with a subspace detector to identify additional events. This will allow us to estimate the increase of the seismicity rate in the local earthquake catalogue

  20. Lyapunov vectors and assimilation in the unstable subspace: theory and applications

    Palatella, Luigi; Carrassi, Alberto; Trevisan, Anna


    Based on a limited number of noisy observations, estimation algorithms provide a complete description of the state of a system at current time. Estimation algorithms that go under the name of assimilation in the unstable subspace (AUS) exploit the nonlinear stability properties of the forecasting model in their formulation. Errors that grow due to sensitivity to initial conditions are efficiently removed by confining the analysis solution in the unstable and neutral subspace of the system, the subspace spanned by Lyapunov vectors with positive and zero exponents, while the observational noise does not disturb the system along the stable directions. The formulation of the AUS approach in the context of four-dimensional variational assimilation (4DVar-AUS) and the extended Kalman filter (EKF-AUS) and its application to chaotic models is reviewed. In both instances, the AUS algorithms are at least as efficient but simpler to implement and computationally less demanding than their original counterparts. As predicted by the theory when error dynamics is linear, the optimal subspace dimension for 4DVar-AUS is given by the number of positive and null Lyapunov exponents, while the EKF-AUS algorithm, using the same unstable and neutral subspace, recovers the solution of the full EKF algorithm, but dealing with error covariance matrices of a much smaller dimension and significantly reducing the computational burden. Examples of the application to a simplified model of the atmospheric circulation and to the optimal velocity model for traffic dynamics are given. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’.

  1. Using CUDA Technology for Defining the Stiffness Matrix in the Subspace of Eigenvectors

    Yu. V. Berchun


    Full Text Available The aim is to improve the performance of solving a problem of deformable solid mechanics through the use of GPGPU. The paper describes technologies for computing systems using both a central and a graphics processor and provides motivation for using CUDA technology as the efficient one.The paper also analyses methods to solve the problem of defining natural frequencies and design waveforms, i.e. an iteration method in the subspace. The method includes several stages. The paper considers the most resource-hungry stage, which defines the stiffness matrix in the subspace of eigenforms and gives the mathematical interpretation of this stage.The GPU choice as a computing device is justified. The paper presents an algorithm for calculating the stiffness matrix in the subspace of eigenforms taking into consideration the features of input data. The global stiffness matrix is very sparse, and its size can reach tens of millions. Therefore, it is represented as a set of the stiffness matrices of the single elements of a model. The paper analyses methods of data representation in the software and selects the best practices for GPU computing.It describes the software implementation using CUDA technology to calculate the stiffness matrix in the subspace of eigenforms. Due to the input data nature, it is impossible to use the universal libraries of matrix computations (cuSPARSE and cuBLAS for loading the GPU. For efficient use of GPU resources in the software implementation, the stiffness matrices of elements are built in the block matrices of a special form. The advantages of using shared memory in GPU calculations are described.The transfer to the GPU computations allowed a twentyfold increase in performance (as compared to the multithreaded CPU-implementation on the model of middle dimensions (degrees of freedom about 2 million. Such an acceleration of one stage speeds up defining the natural frequencies and waveforms by the iteration method in a subspace

  2. Turbo Interference Cancellation/Decoding with Blind Subspace Iterative Channel Estimation for Coded Multi-Carrier DS-CDMA

    XUGuoxing; GANLiangcai; ZHANGXuliang; HUANGTiaxi


    In this paper, we consider subspace based channel estimation methods for Turbo parallel interference cancellation and decoding integrating frequency diversity combining (Turbo FDC-PIC/decoding) over convolutionally coded multi-carrier Direct-sequence Code-division multiple access (DS-CDMA). Applying Turbo principle, we propose blind subspace iterative channel estimation, and apply it to Turbo FDC-PIC/decoding to perform joint channel estimation, detection and decoding. The simu- lation results show that Turbo FDC-PIC/decoding with blind subspace iterative channel estimation has greater performance improvement than that with blind subspace noniterative channel estimation or Pilot symbol aided (PSA) iterative channel estimation, and after a number of iterations, can even obtain performance close to Turbo FDC-PIC/decoding with ideal channel estimation. For example, with the chosen simulation parameter and for the fourth iteration, at the Signal-to-noise rate (SNR) of 7dB, Turbo FDC-PIC/decoding with blind subspace iterative channel estimation acquires the bit error rate of 9×10-4, nearly one order of magnitude lower than that of Turbo FDC-PIC/decoding with PSA iterative channel estimation or with blind subspace noniterative channel estimation. Besides, for blind subspace iterative channel estimation, pilot symbols aren't needed to insert in coded symbols, and therefore data rate is not lowered.

  3. Fast solution of Cahn–Hilliard variational inequalities using implicit time discretization and finite elements

    Bosch, Jessica


    We consider the efficient solution of the Cahn-Hilliard variational inequality using an implicit time discretization, which is formulated as an optimal control problem with pointwise constraints on the control. By applying a semi-smooth Newton method combined with a Moreau-Yosida regularization technique for handling the control constraints we show superlinear convergence in function space. At the heart of this method lies the solution of large and sparse linear systems for which we propose the use of preconditioned Krylov subspace solvers using an effective Schur complement approximation. Numerical results illustrate the competitiveness of this approach. © 2014 Elsevier Inc.

  4. Geometric tools for solving the FDI problem for linear periodic discrete-time systems

    Longhi, Sauro; Monteriù, Andrea


    This paper studies the problem of detecting and isolating faults in linear periodic discrete-time systems. The aim is to design an observer-based residual generator where each residual is sensitive to one fault, whilst remaining insensitive to the other faults that can affect the system. Making use of the geometric tools, and in particular of the outer observable subspace notion, the Fault Detection and Isolation (FDI) problem is formulated and necessary and solvability conditions are given. An algorithmic procedure is described to determine the solution of the FDI problem.

  5. On w-maximal groups

    Gonzalez-Sanchez, Jon


    Let $w = w(x_1,..., x_n)$ be a word, i.e. an element of the free group $F =$ on $n$ generators $x_1,..., x_n$. The verbal subgroup $w(G)$ of a group $G$ is the subgroup generated by the set $\\{w (g_1,...,g_n)^{\\pm 1} | g_i \\in G, 1\\leq i\\leq n \\}$ of all $w$-values in $G$. We say that a (finite) group $G$ is $w$-maximal if $|G:w(G)|> |H:w(H)|$ for all proper subgroups $H$ of $G$ and that $G$ is hereditarily $w$-maximal if every subgroup of $G$ is $w$-maximal. In this text we study $w$-maximal and hereditarily $w$-maximal (finite) groups.

  6. Interpolation of the discrete logarithm in a finite field of characteristic two by Boolean functions

    Brandstaetter, Nina; Lange, Tanja; Winterhof, Arne


    We obtain bounds on degree, weight, and the maximal Fourier coefficient of Boolean functions interpolating the discrete logarithm in finite fields of characteristic two. These bounds complement earlier results for finite fields of odd characteristic.......We obtain bounds on degree, weight, and the maximal Fourier coefficient of Boolean functions interpolating the discrete logarithm in finite fields of characteristic two. These bounds complement earlier results for finite fields of odd characteristic....

  7. A KAM theorem for infinite--dimensional discrete systems

    Perfetti, P


    Infinite--dimesional, discrete hamiltonian systems of the type kinetic energy + potential energy over ${\\Bbb R}^{{\\Bbb Z}}\\times {\\Bbb T}^{{\\Bbb Z}}$ are studied. The existence of many quasi--periodic motions with a maximal set of nonzero frequencies is shown

  8. Maximizing without difficulty: A modified maximizing scale and its correlates

    Lai, Linda


    ... included in several previous studies. Based on this scale, maximizing is positively correlated with optimism, need for cognition, desire for consistency, risk aversion, intrinsic motivation, self-efficacy and perceived workload, whereas...

  9. Maximizing and customer loyalty: Are maximizers less loyal?

    Linda Lai


    Full Text Available Despite their efforts to choose the best of all available solutions, maximizers seem to be more inclined than satisficers to regret their choices and to experience post-decisional dissonance. Maximizers may therefore be expected to change their decisions more frequently and hence exhibit lower customer loyalty to providers of products and services compared to satisficers. Findings from the study reported here (N = 1978 support this prediction. Maximizers reported significantly higher intentions to switch to another service provider (television provider than satisficers. Maximizers' intentions to switch appear to be intensified and mediated by higher proneness to regret, increased desire to discuss relevant choices with others, higher levels of perceived knowledge of alternatives, and higher ego involvement in the end product, compared to satisficers. Opportunities for future research are suggested.

  10. Are maximizers really unhappy? The measurement of maximizing tendency,

    Dalia L. Diab


    Full Text Available Recent research suggesting that people who maximize are less happy than those who satisfice has received considerable fanfare. The current study investigates whether this conclusion reflects the construct itself or rather how it is measured. We developed an alternative measure of maximizing tendency that is theory-based, has good psychometric properties, and predicts behavioral outcomes. In contrast to the existing maximization measure, our new measure did not correlate with life (dissatisfaction, nor with most maladaptive personality and decision-making traits. We conclude that the interpretation of maximizers as unhappy may be due to poor measurement of the construct. We present a more reliable and valid measure for future researchers to use.

  11. Principles of maximally classical and maximally realistic quantum mechanics

    S M Roy


    Recently Auberson, Mahoux, Roy and Singh have proved a long standing conjecture of Roy and Singh: In 2-dimensional phase space, a maximally realistic quantum mechanics can have quantum probabilities of no more than + 1 complete commuting cets (CCS) of observables coexisting as marginals of one positive phase space density. Here I formulate a stationary principle which gives a nonperturbative definition of a maximally classical as well as maximally realistic phase space density. I show that the maximally classical trajectories are in fact exactly classical in the simple examples of coherent states and bound states of an oscillator and Gaussian free particle states. In contrast, it is known that the de Broglie–Bohm realistic theory gives highly nonclassical trajectories.

  12. Discrete-Event Simulation

    Prateek Sharma


    Full Text Available Abstract Simulation can be regarded as the emulation of the behavior of a real-world system over an interval of time. The process of simulation relies upon the generation of the history of a system and then analyzing that history to predict the outcome and improve the working of real systems. Simulations can be of various kinds but the topic of interest here is one of the most important kind of simulation which is Discrete-Event Simulation which models the system as a discrete sequence of events in time. So this paper aims at introducing about Discrete-Event Simulation and analyzing how it is beneficial to the real world systems.

  13. Discrete Anderson Speckle

    Kondakci, H Esat; Saleh, Bahaa E A


    When a disordered array of coupled waveguides is illuminated with an extended coherent optical field, discrete speckle develops: partially coherent light with a granular intensity distribution on the lattice sites. The same paradigm applies to a variety of other settings in photonics, such as imperfectly coupled resonators or fibers with randomly coupled cores. Through numerical simulations and analytical modeling, we uncover a set of surprising features that characterize discrete speckle in one- and two-dimensional lattices known to exhibit transverse Anderson localization. Firstly, the fingerprint of localization is embedded in the fluctuations of the discrete speckle and is revealed in the narrowing of the spatial coherence function. Secondly, the transverse coherence length (or speckle grain size) is frozen during propagation. Thirdly, the axial coherence depth is independent of the axial position, thereby resulting in a coherence voxel of fixed volume independently of position. We take these unique featu...

  14. Discrete systems and integrability

    Hietarinta, J; Nijhoff, F W


    This first introductory text to discrete integrable systems introduces key notions of integrability from the vantage point of discrete systems, also making connections with the continuous theory where relevant. While treating the material at an elementary level, the book also highlights many recent developments. Topics include: Darboux and Bäcklund transformations; difference equations and special functions; multidimensional consistency of integrable lattice equations; associated linear problems (Lax pairs); connections with Padé approximants and convergence algorithms; singularities and geometry; Hirota's bilinear formalism for lattices; intriguing properties of discrete Painlevé equations; and the novel theory of Lagrangian multiforms. The book builds the material in an organic way, emphasizing interconnections between the various approaches, while the exposition is mostly done through explicit computations on key examples. Written by respected experts in the field, the numerous exercises and the thoroug...

  15. Discrete Classical Electromagnetic Fields

    De Souza, M M


    The classical electromagnetic field of a spinless point electron is described in a formalism with extended causality by discrete finite transverse point-vector fields with discrete and localized point interactions. These fields are taken as a classical representation of photons, ``classical photons". They are all transversal photons; there are no scalar nor longitudinal photons as these are definitely eliminated by the gauge condition. The angular distribution of emitted photons coincides with the directions of maximum emission in the standard formalism. The Maxwell formalism and its standard field are retrieved by the replacement of these discrete fields by their space-time averages, and in this process scalar and longitudinal photons are necessarily created and added. Divergences and singularities are by-products of this averaging process. This formalism enlighten the meaning and the origin of the non-physical photons, the ones that violate the Lorentz condition in manifestly covariant quantization methods.

  16. Performance evaluation of block-diagonal preconditioners for the divergence-conforming B-spline discretization of the Stokes system

    Côrtes, A.M.A.


    The recently introduced divergence-conforming B-spline discretizations allow the construction of smooth discrete velocity–pressure pairs for viscous incompressible flows that are at the same time inf-sup stable and pointwise divergence-free. When applied to discretized Stokes equations, these spaces generate a symmetric and indefinite saddle-point linear system. Krylov subspace methods are usually the most efficient procedures to solve such systems. One of such methods, for symmetric systems, is the Minimum Residual Method (MINRES). However, the efficiency and robustness of Krylov subspace methods is closely tied to appropriate preconditioning strategies. For the discrete Stokes system, in particular, block-diagonal strategies provide efficient preconditioners. In this article, we compare the performance of block-diagonal preconditioners for several block choices. We verify how the eigenvalue clustering promoted by the preconditioning strategies affects MINRES convergence. We also compare the number of iterations and wall-clock timings. We conclude that among the building blocks we tested, the strategy with relaxed inner conjugate gradients preconditioned with incomplete Cholesky provided the best results.

  17. Discrete flavor symmetry and minimal seesaw mechanism

    Nam, K H; Siyeon, Kim


    This work proposes a neutrino mass model that is derived using the minimal seesaw mechanism which contains only two right-handed neutrinos, under the non-abelian discrete flavor symmetry $\\mathbb{S}_4\\otimes\\mathbb{Z}_2$. Two standard model doublets, $L_\\mu$ and $L_\\tau$, are assigned simultaneously to a $\\mathbf{2}$ representation of $\\mathbb{S}_4$. When the scalar fields introduced in this model, addition to the Standard Model Higgs, and the leptons are coupled within the symmetry, the seesaw mechanism results in the tri-bi-maximal neutrino mixing. This study examined the possible deviations from TBM mixing related to the experimental data.

  18. Introductory discrete mathematics

    Balakrishnan, V K


    This concise text offers an introduction to discrete mathematics for undergraduate students in computer science and mathematics. Mathematics educators consider it vital that their students be exposed to a course in discrete methods that introduces them to combinatorial mathematics and to algebraic and logical structures focusing on the interplay between computer science and mathematics. The present volume emphasizes combinatorics, graph theory with applications to some stand network optimization problems, and algorithms to solve these problems.Chapters 0-3 cover fundamental operations involv

  19. Discrete breathers in crystals

    Dmitriev, S. V.; Korznikova, E. A.; Baimova, Yu A.; Velarde, M. G.


    It is well known that periodic discrete defect-containing systems, in addition to traveling waves, support vibrational defect-localized modes. It turned out that if a periodic discrete system is nonlinear, it can support spatially localized vibrational modes as exact solutions even in the absence of defects. Since the nodes of the system are all on equal footing, it is only through the special choice of initial conditions that a group of nodes can be found on which such a mode, called a discrete breather (DB), will be excited. The DB frequency must be outside the frequency range of the small-amplitude traveling waves. Not resonating with and expending no energy on the excitation of traveling waves, a DB can theoretically conserve its vibrational energy forever provided no thermal vibrations or other perturbations are present. Crystals are nonlinear discrete systems, and the discovery in them of DBs was only a matter of time. It is well known that periodic discrete defect-containing systems support both traveling waves and vibrational defect-localized modes. It turns out that if a periodic discrete system is nonlinear, it can support spatially localized vibrational modes as exact solutions even in the absence of defects. Because the nodes of the system are all on equal footing, only a special choice of the initial conditions allows selecting a group of nodes on which such a mode, called a discrete breather (DB), can be excited. The DB frequency must be outside the frequency range of small-amplitude traveling waves. Not resonating with and expending no energy on the excitation of traveling waves, a DB can theoretically preserve its vibrational energy forever if no thermal vibrations or other perturbations are present. Crystals are nonlinear discrete systems, and the discovery of DBs in them was only a matter of time. Experimental studies of DBs encounter major technical difficulties, leaving atomistic computer simulations as the primary investigation tool. Despite

  20. Discrete and computational geometry

    Devadoss, Satyan L


    Discrete geometry is a relatively new development in pure mathematics, while computational geometry is an emerging area in applications-driven computer science. Their intermingling has yielded exciting advances in recent years, yet what has been lacking until now is an undergraduate textbook that bridges the gap between the two. Discrete and Computational Geometry offers a comprehensive yet accessible introduction to this cutting-edge frontier of mathematics and computer science. This book covers traditional topics such as convex hulls, triangulations, and Voronoi diagrams, as well a

  1. Deformed discrete symmetries

    Arzano, Michele; Kowalski-Glikman, Jerzy


    We construct discrete symmetry transformations for deformed relativistic kinematics based on group valued momenta. We focus on the specific example of κ-deformations of the Poincaré algebra with associated momenta living on (a sub-manifold of) de Sitter space. Our approach relies on the description of quantum states constructed from deformed kinematics and the observable charges associated with them. The results we present provide the first step towards the analysis of experimental bounds on the deformation parameter κ to be derived via precision measurements of discrete symmetries and CPT.

  2. Maximizing ROI with yield management

    Neil Snyder


    .... the technology is based on the concept of yield management, which aims to sell the right product to the right customer at the right price and the right time therefore maximizing revenue, or yield...

  3. Are CEOs Expected Utility Maximizers?

    John List; Charles Mason


    Are individuals expected utility maximizers? This question represents much more than academic curiosity. In a normative sense, at stake are the fundamental underpinnings of the bulk of the last half-century's models of choice under uncertainty. From a positive perspective, the ubiquitous use of benefit-cost analysis across government agencies renders the expected utility maximization paradigm literally the only game in town. In this study, we advance the literature by exploring CEO's preferen...

  4. Gaussian maximally multipartite entangled states

    Facchi, Paolo; Lupo, Cosmo; Mancini, Stefano; Pascazio, Saverio


    We introduce the notion of maximally multipartite entangled states (MMES) in the context of Gaussian continuous variable quantum systems. These are bosonic multipartite states that are maximally entangled over all possible bipartitions of the system. By considering multimode Gaussian states with constrained energy, we show that perfect MMESs, which exhibit the maximum amount of bipartite entanglement for all bipartitions, only exist for systems containing n=2 or 3 modes. We further numerically investigate the structure of MMESs and their frustration for n <= 7.

  5. All maximally entangling unitary operators

    Cohen, Scott M. [Department of Physics, Duquesne University, Pittsburgh, Pennsylvania 15282 (United States); Department of Physics, Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213 (United States)


    We characterize all maximally entangling bipartite unitary operators, acting on systems A and B of arbitrary finite dimensions d{sub A}{<=}d{sub B}, when ancillary systems are available to both parties. Several useful and interesting consequences of this characterization are discussed, including an understanding of why the entangling and disentangling capacities of a given (maximally entangling) unitary can differ and a proof that these capacities must be equal when d{sub A}=d{sub B}.

  6. On the maximal diphoton width

    Salvio, Alberto; Strumia, Alessandro; Urbano, Alfredo


    Motivated by the 750 GeV diphoton excess found at LHC, we compute the maximal width into $\\gamma\\gamma$ that a neutral scalar can acquire through a loop of charged fermions or scalars as function of the maximal scale at which the theory holds, taking into account vacuum (meta)stability bounds. We show how an extra gauge symmetry can qualitatively weaken such bounds, and explore collider probes and connections with Dark Matter.

  7. Blind equalization and automatic modulation classification based on subspace for subcarrier MPSK optical communications

    Chen, Dan; Guo, Lin-yuan; Wang, Chen-hao; Ke, Xi-zheng


    Equalization can compensate channel distortion caused by channel multipath effects, and effectively improve convergent of modulation constellation diagram in optical wireless system. In this paper, the subspace blind equalization algorithm is used to preprocess M-ary phase shift keying (MPSK) subcarrier modulation signal in receiver. Mountain clustering is adopted to get the clustering centers of MPSK modulation constellation diagram, and the modulation order is automatically identified through the k-nearest neighbor (KNN) classifier. The experiment has been done under four different weather conditions. Experimental results show that the convergent of constellation diagram is improved effectively after using the subspace blind equalization algorithm, which means that the accuracy of modulation recognition is increased. The correct recognition rate of 16PSK can be up to 85% in any kind of weather condition which is mentioned in paper. Meanwhile, the correct recognition rate is the highest in cloudy and the lowest in heavy rain condition.

  8. Hyperspectral Image Kernel Sparse Subspace Clustering with Spatial Max Pooling Operation

    Zhang, Hongyan; Zhai, Han; Liao, Wenzhi; Cao, Liqin; Zhang, Liangpei; Pižurica, Aleksandra


    In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.

  9. Breast Cancer Classification From Histological Images with Multiple Features and Random Subspace Classifier Ensemble

    Zhang, Yungang; Zhang, Bailing; Lu, Wenjin


    Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and Completed Local Binary Patterns (CLBP), respectively. The three different features are combined together and used for classification. A classifier ensemble approach, called Random Subspace Ensemble (RSE), are used to select and aggregate a set of base neural network classifiers for classification. The proposed multiple features and random subspace ensemble offer the classification rate 95.22% on a publically available breast cancer image dataset, which compares favorably with the previously published result 93.4%.

  10. On the Kalman Filter error covariance collapse into the unstable subspace

    Trevisan, A.; Palatella, L.


    When the Extended Kalman Filter is applied to a chaotic system, the rank of the error covariance matrices, after a sufficiently large number of iterations, reduces to N+ + N0 where N+ and N0 are the number of positive and null Lyapunov exponents. This is due to the collapse into the unstable and neutral tangent subspace of the solution of the full Extended Kalman Filter. Therefore the solution is the same as the solution obtained by confining the assimilation to the space spanned by the Lyapunov vectors with non-negative Lyapunov exponents. Theoretical arguments and numerical verification are provided to show that the asymptotic state and covariance estimates of the full EKF and of its reduced form, with assimilation in the unstable and neutral subspace (EKF-AUS) are the same. The consequences of these findings on applications of Kalman type Filters to chaotic models are discussed.

  11. Active subspace approach to reliability and safety assessments of small satellite separation

    Hu, Xingzhi; Chen, Xiaoqian; Zhao, Yong; Tuo, Zhouhui; Yao, Wen


    Ever-increasing launch of small satellites demands an effective and efficient computer-aided analysis approach to shorten the ground test cycle and save the economic cost. However, the multiple influencing factors hamper the efficiency and accuracy of separation reliability assessment. In this study, a novel evaluation approach based on active subspace identification and response surface construction is established and verified. The formulation of small satellite separation is firstly derived, including equations of motion, separation and gravity forces, and quantity of interest. The active subspace reduces the dimension of uncertain inputs with minimum precision loss and a 4th degree multivariate polynomial regression (MPR) using cross validation is hand-coded for the propagation and error analysis. A common spring separation of small satellites is employed to demonstrate the accuracy and efficiency of the approach, which exhibits its potential use in widely existing needs of satellite separation analysis.

  12. Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption

    Zheping Yan


    Full Text Available A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.

  13. The Many Faces of the Subspace Theorem (after Adamczewski, Bugeaud, Corvaja, Zannier...)

    Bilu, Yuri


    During the last decade the Subspace Theorem found several quite unexpected applications, mainly in the Diophantine Analysis and in the Transcendence Theory. Among the great variety of spectacular results, I have chosen several which are technically simpler and which allow one to appreciate how miraculously does the Subspace Theorem emerge in numerous situations, implying beautiful solutions to difficult problems hardly anybody hoped to solve so easily. The three main topics discussed in this article are: the work of Adamczewski and Bugeaud on complexity of algebraic numbers; the work of Corvaja and Zannier on Diophantine equations with power sums; the work of Corvaja and Zannier on integral points on curves and surfaces, and the subsequent development due to Levin and Autissier. In particular, we give a complete proof of the beautiful theorem of Levin and Autissier: an affine surface with 4 (or more) properly intersecting ample divisors at infinity cannot have a Zariski dense set of integral points.

  14. Quantum gate between logical qubits in decoherence-free subspace implemented with trapped ions

    Ivanov, Peter A; Singer, Kilian; Schmidt-Kaler, Ferdinand


    We propose an efficient technique for the implementation of a geometric phase gate in a decoherence-free subspace with trapped ions. In this scheme, the quantum information is encoded in the Zeeman sublevels of the ground state and two physical qubits are used to make up one logical qubit with ultra long coherence time. The physical realization of a geometric phase gate between two logic qubits is performed with four ions in a linear crystal simultaneously interacting with single laser beam. We investigate in detail the robustness of the scheme with respect to the right choice of the trap frequency and provide a detailed analysis of error sources, taking into account the experimental conditions. Furthermore, possible applications for the generation of cluster states for larger numbers of ions within the decoherence-free subspace are presented.

  15. Cumulant-Based Coherent Signal Subspace Method for Bearing and Range Estimation

    Bourennane Salah


    Full Text Available A new method for simultaneous range and bearing estimation for buried objects in the presence of an unknown Gaussian noise is proposed. This method uses the MUSIC algorithm with noise subspace estimated by using the slice fourth-order cumulant matrix of the received data. The higher-order statistics aim at the removal of the additive unknown Gaussian noise. The bilinear focusing operator is used to decorrelate the received signals and to estimate the coherent signal subspace. A new source steering vector is proposed including the acoustic scattering model at each sensor. Range and bearing of the objects at each sensor are expressed as a function of those at the first sensor. This leads to the improvement of object localization anywhere, in the near-field or in the far-field zone of the sensor array. Finally, the performances of the proposed method are validated on data recorded during experiments in a water tank.

  16. Universal holonomic quantum gates in decoherence-free subspace on superconducting circuits

    Xue, Zheng-Yuan; Zhou, Jian; Wang, Z. D.


    To implement a set of universal quantum logic gates based on non-Abelian geometric phases, it is conventional wisdom that quantum systems beyond two levels are required, which is extremely difficult to fulfill for superconducting qubits and appears to be a main reason why only single-qubit gates were implemented in a recent experiment [A. A. Abdumalikov, Jr. et al., Nature (London) 496, 482 (2013), 10.1038/nature12010]. Here we propose to realize nonadiabatic holonomic quantum computation in decoherence-free subspace on circuit QED, where one can use only the two levels in transmon qubits, a usual interaction, and a minimal resource for the decoherence-free subspace encoding. In particular, our scheme not only overcomes the difficulties encountered in previous studies but also can still achieve considerably large effective coupling strength, such that high-fidelity quantum gates can be achieved. Therefore, the present scheme makes realizing robust holonomic quantum computation with superconducting circuits very promising.

  17. Time-dependent global sensitivity analysis with active subspaces for a lithium ion battery model

    Constantine, Paul G


    Renewable energy researchers use computer simulation to aid the design of lithium ion storage devices. The underlying models contain several physical input parameters that affect model predictions. Effective design and analysis must understand the sensitivity of model predictions to changes in model parameters, but global sensitivity analyses become increasingly challenging as the number of input parameters increases. Active subspaces are part of an emerging set of tools to reveal and exploit low-dimensional structures in the map from high-dimensional inputs to model outputs. We extend a linear model-based heuristic for active subspace discovery to time-dependent processes and apply the resulting technique to a lithium ion battery model. The results reveal low-dimensional structure that a designer may exploit to efficiently study the relationship between parameters and predictions.

  18. Quantum computing in decoherence-free subspaces with superconducting charge qubits

    Feng Zhibo [National Laboratory of Solid State Microstructures, Department of Physics, Nanjing University, Nanjing 210093 (China); Institute for Condensed Matter Physics, School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510631 (China); Zhang Xinding [Institute for Condensed Matter Physics, School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510631 (China)], E-mail:


    Taking into account the main noises in superconducting charge qubits (SCQs), we propose a feasible scheme to realize quantum computing (QC) in a specially-designed decoherence-free subspace (DFS). In our scheme two physical qubits are connected with a common inductance to form a strong coupling subsystem, which acts as a logical qubit. Benefiting from the well-designed DFS, our scheme is helpful to suppress certain decoherence effects.

  19. Grothendieck-Lidskii theorem for subspaces and factor spaces of L_p-spaces

    Reinov, Oleg


    In 1955, A. Grothendieck has shown that if the linear operator $T$ in a Banach subspace of an $L_\\infty$-space is 2/3-nuclear then the trace of $T$ is well defined and is equal to the sum of all eigenvalues $\\{\\mu_k(T)\\}$ of $T.$ V.B. Lidski\\v{\\i}, in 1959, proved his famous theorem on the coincidence of the trace of the $S_1$-operator in $L_2(\

  20. Subspace based adaptive denoising of surface EMG from neurological injury patients

    Liu, Jie; Ying, Dongwen; Zev Rymer, William; Zhou, Ping


    Objective: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. Approach: The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. Main results: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. Significance: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.

  1. Cavity quantum networks for quantum information processing in decoherence-free subspace

    Hua WEI; Zhi-jiao DENG; Wan-li YANG; Fei ZHOU


    We give a brief review on the quantum infor- mation processing in decoherence-free subspace (DFS). We show how to realize the initialization of the entangled quantum states, information transfer and teleportation of quantum states, two-qubit Grover search and how to construct the quantum network in DFS, within the cav- ity QED regime based on a cavity-assisted interaction by single-photon pulses.

  2. Decoherence free in subspace using Na at C{sub 60} as quantum qubit

    Zeng Xianghua; Bi Qiao; Guo Guangcan; Ruda, H.E


    An approach of quantum computing based on endohedral metallofullerenes has been discussed, which includes the construction of c{sup n}-Not logic gates and decoherence-free in the projected subspace to protect against the decoherence from the interaction with environment. As the special structure of Na at C{sub 60}, symmetrically alignment of n Na at C{sub 60} along z direction, we can construct the multiqubits under the control of the STM setups.

  3. The discrete Feynman integral

    Dorlas, T. C.; Thomas, E. G. F.


    We construct a genuine Radon measure with values in B(l(2)(Z(d))) on the set of paths in Z(d) representing Feynman's integral for the discrete Laplacian on l(2)(Z(d)), and we prove the Feynman integral formula for the solutions of the Schrodinger equation with Hamiltonian H=-1/2 Delta+ V, where Delt

  4. Discrete time process algebra

    Bergstra, J.A.; Baeten, J.C.M.


    The axiom system ACP of [BeK84a] was extended with real time features in [BaB91]. Here we proceed to define a discrete time extension of ACP, along the lines of ATP [NiS94]. We present versions based on relative timing and on absolute timing. Both approaches are integrated using parametric timing. T

  5. Discrete gauge theories

    de Wild Propitius, M.D.F.; Bais, F.A.


    In these lectures, we present a self-contained treatment of planar gauge theories broken down to some finite residual gauge group $H$ via the Higgs mechanism. The main focus is on the discrete $H$ gauge theory describing the long distance physics of such a model. The spectrum features global $H$ cha

  6. Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

    Yu, Hang; Xu, Luping; Feng, Dongzhu; He, Xiaochuan


    Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time.

  7. A Rank-Constrained Matrix Representation for Hypergraph-Based Subspace Clustering

    Yubao Sun


    Full Text Available This paper presents a novel, rank-constrained matrix representation combined with hypergraph spectral analysis to enable the recovery of the original subspace structures of corrupted data. Real-world data are frequently corrupted with both sparse error and noise. Our matrix decomposition model separates the low-rank, sparse error, and noise components from the data in order to enhance robustness to the corruption. In order to obtain the desired rank representation of the data within a dictionary, our model directly utilizes rank constraints by restricting the upper bound of the rank range. An alternative projection algorithm is proposed to estimate the low-rank representation and separate the sparse error from the data matrix. To further capture the complex relationship between data distributed in multiple subspaces, we use hypergraph to represent the data by encapsulating multiple related samples into one hyperedge. The final clustering result is obtained by spectral decomposition of the hypergraph Laplacian matrix. Validation experiments on the Extended Yale Face Database B, AR, and Hopkins 155 datasets show that the proposed method is a promising tool for subspace clustering.

  8. Krylov subspace methods for complex non-Hermitian linear systems. Thesis

    Freund, Roland W.


    We consider Krylov subspace methods for the solution of large sparse linear systems Ax = b with complex non-Hermitian coefficient matrices. Such linear systems arise in important applications, such as inverse scattering, numerical solution of time-dependent Schrodinger equations, underwater acoustics, eddy current computations, numerical computations in quantum chromodynamics, and numerical conformal mapping. Typically, the resulting coefficient matrices A exhibit special structures, such as complex symmetry, or they are shifted Hermitian matrices. In this paper, we first describe a Krylov subspace approach with iterates defined by a quasi-minimal residual property, the QMR method, for solving general complex non-Hermitian linear systems. Then, we study special Krylov subspace methods designed for the two families of complex symmetric respectively shifted Hermitian linear systems. We also include some results concerning the obvious approach to general complex linear systems by solving equivalent real linear systems for the real and imaginary parts of x. Finally, numerical experiments for linear systems arising from the complex Helmholtz equation are reported.

  9. Operational Modal Analysis Based on Subspace Algorithm with an Improved Stabilization Diagram Method

    Shiqiang Qin


    Full Text Available Subspace-based algorithms for operational modal analysis have been extensively studied in the past decades. In the postprocessing of subspace-based algorithms, the stabilization diagram is often used to determine modal parameters. In this paper, an improved stabilization diagram is proposed for stochastic subspace identification. Specifically, first, a model order selection method based on singular entropy theory is proposed. The singular entropy increment is calculated from nonzero singular values of the output covariance matrix. The corresponding model order can be selected when the variation of singular entropy increment approaches to zero. Then, the stabilization diagram with confidence intervals which is established using the uncertainty of modal parameter is presented. Finally, a simulation example of a four-story structure and a full-scale cable-stayed footbridge application is employed to illustrate the improved stabilization diagram method. The study demonstrates that the model order can be reasonably determined by the proposed method. The stabilization diagram with confidence intervals can effectively remove the spurious modes.

  10. Maximization

    A. Garmroodi Asil


    To further reduce the sulfur dioxide emission of the entire refining process, two scenarios of acid gas or air preheats are investigated when either of them is used simultaneously with the third enrichment scheme. The maximum overall sulfur recovery efficiency and highest combustion chamber temperature is slightly higher for acid gas preheats but air preheat is more favorable because it is more benign. To the best of our knowledge, optimization of the entire GTU + enrichment section and SRU processes has not been addressed previously.

  11. Algebraic curves of maximal cyclicity

    Caubergh, Magdalena; Dumortier, Freddy


    The paper deals with analytic families of planar vector fields, studying methods to detect the cyclicity of a non-isolated closed orbit, i.e. the maximum number of limit cycles that can locally bifurcate from it. It is known that this multi-parameter problem can be reduced to a single-parameter one, in the sense that there exist analytic curves in parameter space along which the maximal cyclicity can be attained. In that case one speaks about a maximal cyclicity curve (mcc) in case only the number is considered and of a maximal multiplicity curve (mmc) in case the multiplicity is also taken into account. In view of obtaining efficient algorithms for detecting the cyclicity, we investigate whether such mcc or mmc can be algebraic or even linear depending on certain general properties of the families or of their associated Bautin ideal. In any case by well chosen examples we show that prudence is appropriate.

  12. Discrete mathematics with applications

    Koshy, Thomas


    This approachable text studies discrete objects and the relationsips that bind them. It helps students understand and apply the power of discrete math to digital computer systems and other modern applications. It provides excellent preparation for courses in linear algebra, number theory, and modern/abstract algebra and for computer science courses in data structures, algorithms, programming languages, compilers, databases, and computation.* Covers all recommended topics in a self-contained, comprehensive, and understandable format for students and new professionals * Emphasizes problem-solving techniques, pattern recognition, conjecturing, induction, applications of varying nature, proof techniques, algorithm development and correctness, and numeric computations* Weaves numerous applications into the text* Helps students learn by doing with a wealth of examples and exercises: - 560 examples worked out in detail - More than 3,700 exercises - More than 150 computer assignments - More than 600 writing projects*...

  13. Discrete torsion defects

    Brunner, Ilka; Plencner, Daniel


    Orbifolding two-dimensional quantum field theories by a symmetry group can involve a choice of discrete torsion. We apply the general formalism of `orbifolding defects' to study and elucidate discrete torsion for topological field theories. In the case of Landau-Ginzburg models only the bulk sector had been studied previously, and we re-derive all known results. We also introduce the notion of `projective matrix factorisations', show how they naturally describe boundary and defect sectors, and we further illustrate the efficiency of the defect-based approach by explicitly computing RR charges. Roughly half of our results are not restricted to Landau-Ginzburg models but hold more generally, for any topological field theory. In particular we prove that for a pivotal bicategory, any two objects of its orbifold completion that have the same base are orbifold equivalent. Equivalently, from any orbifold theory (including those based on nonabelian groups) the original unorbifolded theory can be be obtained by orbifo...

  14. Discrete Variational Optimal Control

    Jimenez, Fernando; de Diego, David Martin


    This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of optimal control problems through the discretization of variational principles. The key point is to solve the optimal control problem as a variational integrator of a specially constructed higher-dimensional system. The developed framework applies to systems on tangent bundles, Lie groups, underactuated and nonholonomic systems with symmetries, and can approximate either smooth or discontinuous control inputs. The resulting methods inherit the preservation properties of variational integrators and result in numerically robust and easily implementable algorithms. Several theoretical and a practical examples, e.g. the control of an underwater vehicle, will illustrate the application of the proposed approach.

  15. Discrete Variational Optimal Control

    Jiménez, Fernando; Kobilarov, Marin; Martín de Diego, David


    This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of optimal control problems through the discretization of variational principles. The key point is to solve the optimal control problem as a variational integrator of a specially constructed higher dimensional system. The developed framework applies to systems on tangent bundles, Lie groups, and underactuated and nonholonomic systems with symmetries, and can approximate either smooth or discontinuous control inputs. The resulting methods inherit the preservation properties of variational integrators and result in numerically robust and easily implementable algorithms. Several theoretical examples and a practical one, the control of an underwater vehicle, illustrate the application of the proposed approach.

  16. Discrete dynamical models

    Salinelli, Ernesto


    This book provides an introduction to the analysis of discrete dynamical systems. The content is presented by an unitary approach that blends the perspective of mathematical modeling together with the ones of several discipline as Mathematical Analysis, Linear Algebra, Numerical Analysis, Systems Theory and Probability. After a preliminary discussion of several models, the main tools for the study of linear and non-linear scalar dynamical systems are presented, paying particular attention to the stability analysis. Linear difference equations are studied in detail and an elementary introduction of Z and Discrete Fourier Transform is presented. A whole chapter is devoted to the study of bifurcations and chaotic dynamics. One-step vector-valued dynamical systems are the subject of three chapters, where the reader can find the applications to positive systems, Markov chains, networks and search engines. The book is addressed mainly to students in Mathematics, Engineering, Physics, Chemistry, Biology and Economic...

  17. Time Discretization Techniques

    Gottlieb, S.


    The time discretization of hyperbolic partial differential equations is typically the evolution of a system of ordinary differential equations obtained by spatial discretization of the original problem. Methods for this time evolution include multistep, multistage, or multiderivative methods, as well as a combination of these approaches. The time step constraint is mainly a result of the absolute stability requirement, as well as additional conditions that mimic physical properties of the solution, such as positivity or total variation stability. These conditions may be required for stability when the solution develops shocks or sharp gradients. This chapter contains a review of some of the methods historically used for the evolution of hyperbolic PDEs, as well as cutting edge methods that are now commonly used.

  18. Linearity stabilizes discrete breathers

    T R Krishna Mohan; Surajit Sen


    The study of the dynamics of 1D chains with both harmonic and nonlinear interactions, as in the Fermi–Pasta–Ulam (FPU) and related problems, has played a central role in efforts to identify the broad consequences of nonlinearity in these systems. Here we study the dynamics of highly localized excitations, or discrete breathers, which are known to be initiated by the quasistatic stretching of bonds between adjacent particles. We show via dynamical simulations that acoustic waves introduced by the harmonic term stabilize the discrete breather by suppressing the breather’s tendency to delocalize and disperse. We conclude that the harmonic term, and hence acoustic waves, are essential for the existence of localized breathers in these systems.

  19. Lectures on discrete geometry


    Discrete geometry investigates combinatorial properties of configurations of geometric objects. To a working mathematician or computer scientist, it offers sophisticated results and techniques of great diversity and it is a foundation for fields such as computational geometry or combinatorial optimization. This book is primarily a textbook introduction to various areas of discrete geometry. In each area, it explains several key results and methods, in an accessible and concrete manner. It also contains more advanced material in separate sections and thus it can serve as a collection of surveys in several narrower subfields. The main topics include: basics on convex sets, convex polytopes, and hyperplane arrangements; combinatorial complexity of geometric configurations; intersection patterns and transversals of convex sets; geometric Ramsey-type results; polyhedral combinatorics and high-dimensional convexity; and lastly, embeddings of finite metric spaces into normed spaces. Jiri Matousek is Professor of Com...




    The authors study the singular integrals under the Hormander condition and the measure not satisfying the doubling condition. At first, if the corresponding singular integral is bounded from L2 to itseff, it is proved that the maximal singu lar integral is bounded from L∞ to RBMO except that it is infinite μ-a.e. on Rd. A sufficient condition and a necessary condition such that the maximal singular integral is bounded from L2 to itself are also obtained. There is a small gap between the two conditions.

  1. Steerable Discrete Cosine Transform

    Fracastoro, Giulia; Fosson, Sophie; Magli, Enrico


    In image compression, classical block-based separable transforms tend to be inefficient when image blocks contain arbitrarily shaped discontinuities. For this reason, transforms incorporating directional information are an appealing alternative. In this paper, we propose a new approach to this problem, namely, a discrete cosine transform (DCT) that can be steered in any chosen direction. Such transform, called steerable DCT (SDCT), allows to rotate in a flexible way pairs of basis vectors, an...

  2. Discrete Quantum Mechanics

    Odake, Satoru; Sasaki, Ryu


    A comprehensive review of the discrete quantum mechanics with the pure imaginary shifts and the real shifts is presented in parallel with the corresponding results in the ordinary quantum mechanics. The main subjects to be covered are the factorised Hamiltonians, the general structure of the solution spaces of the Schroedinger equation (Crum's theorem and its modification), the shape invariance, the exact solvability in the Schroedinger picture as well as in the Heisenberg picture, the creati...

  3. p-form electromagnetism on discrete spacetimes

    Wise, Derek K [Department of Mathematics, University of California, Riverside, CA 92521 (United States)


    We investigate p-form electromagnetism-with the Maxwell and Kalb-Ramond fields as lowest-order cases-on discrete spacetimes, including not only the regular lattices commonly used in lattice gauge theory, but also more general examples. After constructing a maximally general model of discrete spacetime suitable for our purpose-a chain complex equipped with an inner product on (p + 1)-cochains-we study both the classical and quantum versions of the theory, with either R or U(1) as gauge group. We find results-such as a 'p-form Bohm-Aharonov effect'-that depend in interesting ways on the cohomology of spacetime. We quantize the theory via the Euclidean path integral formalism, where the natural kernels in the U(1) theory are not Gaussians but theta functions. As a special case of the general theory, we show that p-form electromagnetism in p + 1 dimensions has an exact solution which reduces when p = 1 to the Abelian case of 2D Yang-Mills theory as studied by Migdal and Witten. Our main result describes p-form electromagnetism as a 'chain field theory'-a theory analogous to a topological quantum field theory, but with chain complexes replacing manifolds. This makes precise a notion of time evolution in the context of discrete spacetimes of arbitrary topology.

  4. Understanding maximal repetitions in strings

    Crochemore, Maxime


    The cornerstone of any algorithm computing all repetitions in a string of length n in O(n) time is the fact that the number of runs (or maximal repetitions) is O(n). We give a simple proof of this result. As a consequence of our approach, the stronger result concerning the linearity of the sum of exponents of all runs follows easily.

  5. From ordinary to discrete quantum mechanics: The Charlier oscillator and its coalgebra symmetry

    Latini, D.; Riglioni, D.


    The coalgebraic structure of the harmonic oscillator is used to underline possible connections between continuous and discrete superintegrable models which can be described in terms of SUSY discrete quantum mechanics. A set of 1-parameter algebraic transformations is introduced in order to generate a discrete representation for the coalgebraic harmonic oscillator. This set of transformations is shown to play a role in the generalization of classical orthogonal polynomials to the realm of discrete orthogonal polynomials in the Askey scheme. As an explicit example the connection between Hermite and Charlier oscillators, that share the same coalgebraic structure, is presented and a two-dimensional maximally superintegrable version of the Charlier oscillator is constructed.

  6. Stationary Scalar Clouds Around Maximally Rotating Linear Dilaton Black Holes

    Sakalli, I


    We investigate the wave dynamics of a charged massive scalar field propagating in a maximally rotating (extremal) linear dilaton black hole geometry. We prove the existence of a discrete and infinite family of resonances describing non-decaying (stationary) scalar configurations (clouds) enclosing these rapidly rotating black holes. The results obtained signal the potential stationary scalar field distributions (dark matter) around the extremal linear dilaton black holes. In particular, we analytically compute the effective height of those clouds above the center of the black hole.

  7. Adaptive Subspace-based Inverse Projections via Division into Multiple Sub-problems for Missing Image Data Restoration.

    Ogawa, Takahiro; Haseyama, Miki


    This paper presents adaptive subspace-based inverse projections via division into multiple sub-problems (ASIP-DIMS) for missing image data restoration. In the proposed method, a target problem for estimating missing image data is divided into multiple sub-problems, and each sub-problem is iteratively solved with constraints of other known image data. By projection into a subspace model of image patches, the solution of each subproblem is calculated, where we call this procedure "subspacebased inverse projection" for simplicity. The proposed method can use higher-dimensional subspaces for finding unique solutions in each sub-problem, and successful restoration becomes feasible since a high level of image representation performance can be preserved. This is the biggest contribution of this paper. Furthermore, the proposed method generates several subspaces from known training examples and enables derivation of a new criterion in the above framework to adaptively select the optimal subspace for each target patch. In this way, the proposed method realizes missing image data restoration using ASIP-DIMS. Since our method can estimate any kind of missing image data, its potential in two image restoration tasks, image inpainting and super-resolution, based on several methods for multivariate analysis is also shown in this paper.

  8. Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis

    Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan


    It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus


    Y. Xu


    Full Text Available This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM. The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.

  10. Investigation into on-road vehicle parameter identification based on subspace methods

    Dong, Guangming; Chen, Jin; Zhang, Nong


    The randomness of road-tyre excitations can excite the low frequency ride vibrations of bounce, pitch and roll modes of an on-road vehicle. In this paper, modal parameters and mass moments of inertia of an on-road vehicle are estimated with an acceptable accuracy only by measuring accelerations of vehicle sprung mass and unsprung masses, which is based on subspace identification methods. The vehicle bounce, pitch and roll modes are characterized by their large damping (damping ratio 0.2-0.3). Two kinds of subspace identification methods, one that uses input/output data and the other that uses output data only, are compared for the highly damped modes. It is shown that, when the same data length is given, larger error of modal identification results can be clearly observed for the method using output data only; while additional use of input data will significantly reduce estimation variance. Instead of using tyre forces as inputs, which are difficult to be measured or estimated, vertical accelerations of unsprung masses are used as inputs. Theoretical analysis and Monte Carlo experiments show that, when the vehicle speed is not very high, subspace identification method using accelerations of unsprung masses as inputs can give more accurate results compared with the method using road-tyre forces as inputs. After the modal parameters are identified, and if vehicle mass and its center of gravity are pre-determined, roll and pitch moments of inertia of an on-road vehicle can be directly computed using the identified frequencies only, without requiring accurate estimation of mode shape vectors and multi-variable optimization algorithms.

  11. Classification of Photogrammetric Point Clouds of Scaffolds for Construction Site Monitoring Using Subspace Clustering and PCA

    Xu, Y.; Tuttas, S.; Heogner, L.; Stilla, U.


    This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM). The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA) algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.

  12. Origin of Constrained Maximal CP Violation in Flavor Symmetry

    He, Hong-Jian; Xu, Xun-Jie


    Current data from neutrino oscillation experiments are in good agreement with $\\delta=-\\pi/2$ and $\\theta_{23} = \\pi/4$. We define the notion of "constrained maximal CP violation" for these features and study their origin in flavor symmetry models. We give various parametrization-independent definitions of constrained maximal CP violation and present a theorem on how it can be generated. This theorem takes advantage of residual symmetries in the neutrino and charged lepton mass matrices, and states that, up to a few exceptions, $\\delta=\\pm\\pi/2$ and $\\theta_{23} = \\pi/4$ are generated when those symmetries are real. The often considered $\\mu$-$\\tau$ reflection symmetry, as well as specific discrete subgroups of $O(3)$, are special case of our theorem.

  13. Dynamically Disordered Quantum Walk as a Maximal Entanglement Generator

    Vieira, Rafael; Amorim, Edgard P. M.; Rigolin, Gustavo


    We show that the entanglement between the internal (spin) and external (position) degrees of freedom of a qubit in a random (dynamically disordered) one-dimensional discrete time quantum random walk (QRW) achieves its maximal possible value asymptotically in the number of steps, outperforming the entanglement attained by using ordered QRW. The disorder is modeled by introducing an extra random aspect to QRW, a classical coin that randomly dictates which quantum coin drives the system’s time evolution. We also show that maximal entanglement is achieved independently of the initial state of the walker, study the number of steps the system must move to be within a small fixed neighborhood of its asymptotic limit, and propose two experiments where these ideas can be tested.

  14. Discrete epidemic models.

    Brauer, Fred; Feng, Zhilan; Castillo-Chavez, Carlos


    The mathematical theory of single outbreak epidemic models really began with the work of Kermack and Mackendrick about decades ago. This gave a simple answer to the long-standing question of why epidemics woould appear suddenly and then disappear just as suddenly without having infected an entire population. Therefore it seemed natural to expect that theoreticians would immediately proceed to expand this mathematical framework both because the need to handle recurrent single infectious disease outbreaks has always been a priority for public health officials and because theoreticians often try to push the limits of exiting theories. However, the expansion of the theory via the inclusion of refined epidemiological classifications or through the incorporation of categories that are essential for the evaluation of intervention strategies, in the context of ongoing epidemic outbreaks, did not materialize. It was the global threat posed by SARS in that caused theoreticians to expand the Kermack-McKendrick single-outbreak framework. Most recently, efforts to connect theoretical work to data have exploded as attempts to deal with the threat of emergent and re-emergent diseases including the most recent H1N1 influenza pandemic, have marched to the forefront of our global priorities. Since data are collected and/or reported over discrete units of time, developing single outbreak models that fit collected data naturally is relevant. In this note, we introduce a discrete-epidemic framework and highlight, through our analyses, the similarities between single-outbreak comparable classical continuous-time epidemic models and the discrete-time models introduced in this note. The emphasis is on comparisons driven by expressions for the final epidemic size.

  15. Discrete Dynamics Lab

    Wuensche, Andrew

    DDLab is interactive graphics software for creating, visualizing, and analyzing many aspects of Cellular Automata, Random Boolean Networks, and Discrete Dynamical Networks in general and studying their behavior, both from the time-series perspective — space-time patterns, and from the state-space perspective — attractor basins. DDLab is relevant to research, applications, and education in the fields of complexity, self-organization, emergent phenomena, chaos, collision-based computing, neural networks, content addressable memory, genetic regulatory networks, dynamical encryption, generative art and music, and the study of the abstract mathematical/physical/dynamical phenomena in their own right.


    张灿辉; 冯伟; 黄黔


    The following is proved: 1 ) The linear independence of assumed stress modes is the necessary and sufficient condition for the nonsingular fiexibility matrix; 2) The equivalent assumed stress modes lead to the identical hybrid element. The Hilbert stress subspace of the assumed stress modes is established. So, it is easy to derive the equivalent orthogonal normal stress modes by Schmidt 's method. Because of the resulting diagonal fiexibility matrix, the identical hybrid element is free from the complex matrix inversion so that the hybrid efficiency is improved greatly. The numerical examples show that the method is effective.

  17. Two stage DOA and Fundamental Frequency Estimation based on Subspace Techniques

    Zhou, Zhenhua; Christensen, Mads Græsbøll; So, Hing-Cheung


    optimally weighted harmonic multiple signal classification (MCOW-HMUSIC) estimator is devised for the estimation of fundamental frequencies. Secondly, the spatio- temporal multiple signal classification (ST-MUSIC) estimator is proposed for the estimation of DOA with the estimated frequencies. Statistical......In this paper, the problem of fundamental frequency and direction-of-arrival (DOA) estimation for multi-channel harmonic sinusoidal signal is addressed. The estimation procedure consists of two stages. Firstly, by making use of the subspace technique and Markov-based eigenanalysis, a multi- channel...... evaluation with synthetic signals shows the high accuracy of the proposed methods compared with their non-weighting versions....

  18. A Subspace Identification Method for Detecting Abnormal Behavior in HVAC Systems

    Dimitris Sklavounos


    Full Text Available A method for the detection of abnormal behavior in HVAC systems is presented. The method combines deterministic subspace identification for each zone independently to create a system model that produces the anticipated zone’s temperature and the sequential test CUSUM algorithm to detect drifts of the rate of change of the difference between the real and the anticipated measurements. Simulation results regarding the detection of infiltration heat losses and the detection of exogenous heat gains such as fire demonstrate the effectiveness of the proposed method.

  19. Investigation of the stochastic subspace identification method for on-line wind turbine tower monitoring

    Dai, Kaoshan; Wang, Ying; Lu, Wensheng; Ren, Xiaosong; Huang, Zhenhua


    Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.

  20. Adaptive coherence estimator based on the Krylov subspace technique for airborne radar

    Weijian Liu; Wenchong Xie; Haibo Tong; Honglin Wang; Cui Zhou; Yongliang Wang


    A novel adaptive detector for airborne radar space-time adaptive detection (STAD) in partial y homogeneous environments is proposed. The novel detector combines the numerical y stable Krylov subspace technique and diagonal loading technique, and it uses the framework of the adaptive coherence estimator (ACE). It can effectively detect a target with low sample support. Compared with its natural competitors, the novel detector has higher proba-bility of detection (PD), especial y when the number of the training data is low. Moreover, it is shown to be practical y constant false alarm rate (CFAR).

  1. A subspace-based parameter estimation algorithm for Nakagami-m fading channels

    Dianat, Sohail; Rao, Raghuveer


    Estimation of channel fading parameters is an important task in the design of communication links such as maximum ratio combining (MRC). The MRC weights are directly related to the fading channel coefficients. In this paper, we propose a subspace based parameter estimation algorithm for the estimation of the parameters of Nakagami-m fading channels in the presence of additive white Gaussian noise. Comparisons of our proposed approach are made with other techniques available in the literature. The performance of the algorithm with respect to the Cramer-Rao bound (CRB) is investigated. Computer simulation results for different signal to noise ratios (SNR) are presented.


    Fan Da; Cao Zhigang


    This paper investigates Carrier Frequency Offset (CFO), estimation in the uplink of the Orthogonal Frequency-Division Multiple Access (OFDMA) systems with the interleaved subcarrier assignment. CFOs between the transmitters and the uplink receiver will destroy orthogonality among different subcarriers, hence resulting in inter-carrier interference and multiuser interference. A two-stage frequency offset estimation algorithm based on subspace processing is proposed. The main advantage of the proposed method is that it can obtain the CFOs of all users simultaneously using only one OFDMA block. Compared with the previously known methods, it not only has a relatively low implementation complexity but is also suitable for random subchannel assignment.

  3. The wide-band coherent signal-subspace processing based on propagator method

    ZHI Wanjun; LI Zhishun


    The narrow band propagator method is introduced to the wide-band coherent signal-subspace processing in the direction finding problem. A new technique that needs no direction pre-estimation or matrix decomposition is presented to compute the focusing matrices, so the focusing matrices are robust and the computation. is simple. Then, the propagator method is extended to the focused covariance matrix to find the directions of the sources. The whole estimation process avoids the rather expensive matrix decomposition, and the results of simulations proved the effectiveness of the new method.

  4. A Comfort-Aware Energy Efficient HVAC System Based on the Subspace Identification Method

    O. Tsakiridis


    Full Text Available A proactive heating method is presented aiming at reducing the energy consumption in a HVAC system while maintaining the thermal comfort of the occupants. The proposed technique fuses time predictions for the zones’ temperatures, based on a deterministic subspace identification method, and zones’ occupancy predictions, based on a mobility model, in a decision scheme that is capable of regulating the balance between the total energy consumed and the total discomfort cost. Simulation results for various occupation-mobility models demonstrate the efficiency of the proposed technique.

  5. Quantum Gate Operations in Decoherence-Free Subspace with Superconducting Charge Qubits inside a Cavity

    WANG Yi-Min; ZHOU Yan-Li; LIANG Lin-Mei; LI Cheng-Zu


    We propose a feasible scheme to achieve universal quantum gate operations in decoherence-free subspace with superconducting charge qubits placed in a microwave cavity.Single-logic-qubit gates can be realized with cavity assisted interaction, which possesses the advantages of unconventional geometric gate operation.The two-logic-qubit controlled-phase gate between subsystems can be constructed with the help of a variable electrostatic transformer, The collective decoherence can be successfully avoided in our well-designed system.Moreover, GHZ state for logical qubits can also be easily produced in this system.

  6. Prewhitening for Rank-Deficient Noise in Subspace Methods for Noise Reduction

    Hansen, Per Christian; Jensen, Søren Holdt


    A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, e.g., when the noise...... also for rank deficient noise. We also demonstrate how to formulate this algorithm by means of a quotient ULV decomposition, which allows for faster computation and updating. Finally we apply our algorithm to a problem involving a speech signal contaminated by narrow-band noise....

  7. Prewhitening for Narrow-Band Noise in Subspace Methods for Noise Reduction

    Hansen, Per Christian; Jensen, Søren Holdt


    A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, typically when...... that works also for rank deficient noise. We also demonstrate how to formulate this algorithm by means of a quotient ULV decomposition, which allows for faster computation and updating. Finally we apply our algorithm to a problem involving a speech signal contaminated by narrow-band noise....

  8. Note on maximal distance separable codes

    YANG Jian-sheng; WANG De-xiu; JIN Qing-fang


    In this paper, the maximal length of maximal distance separable(MDS)codes is studied, and a new upper bound formula of the maximal length of MDS codes is obtained. Especially, the exact values of the maximal length of MDS codes in some parameters are given.

  9. Maximization, learning, and economic behavior.

    Erev, Ido; Roth, Alvin E


    The rationality assumption that underlies mainstream economic theory has proved to be a useful approximation, despite the fact that systematic violations to its predictions can be found. That is, the assumption of rational behavior is useful in understanding the ways in which many successful economic institutions function, although it is also true that actual human behavior falls systematically short of perfect rationality. We consider a possible explanation of this apparent inconsistency, suggesting that mechanisms that rest on the rationality assumption are likely to be successful when they create an environment in which the behavior they try to facilitate leads to the best payoff for all agents on average, and most of the time. Review of basic learning research suggests that, under these conditions, people quickly learn to maximize expected return. This review also shows that there are many situations in which experience does not increase maximization. In many cases, experience leads people to underweight rare events. In addition, the current paper suggests that it is convenient to distinguish between two behavioral approaches to improve economic analyses. The first, and more conventional approach among behavioral economists and psychologists interested in judgment and decision making, highlights violations of the rational model and proposes descriptive models that capture these violations. The second approach studies human learning to clarify the conditions under which people quickly learn to maximize expected return. The current review highlights one set of conditions of this type and shows how the understanding of these conditions can facilitate market design.

  10. Curve Evolution in Subspaces and Exploring the Metameric Class of Histogram of Gradient Orientation based Features using Nonlinear Projection Methods

    Tatu, Aditya Jayant

    defined subspace, the N-links bicycle chain space, i.e. the space of curves with equidistant neighboring landmark points. This in itself is a useful shape space for medical image analysis applications. The Histogram of Gradient orientation based features are many in number and are widely used......This thesis deals with two unrelated issues, restricting curve evolution to subspaces and computing image patches in the equivalence class of Histogram of Gradient orientation based features using nonlinear projection methods. Curve evolution is a well known method used in various applications like...... specific requirements like shape priors or a given data model, and due to limitations of the computer, the computed curve evolution forms a path in some finite dimensional subspace of the space of curves. We give methods to restrict the curve evolution to a finite dimensional linear or implicitly defined...

  11. Approximations for Monotone and Non-monotone Submodular Maximization with Knapsack Constraints

    Kulik, Ariel; Tamir, Tami


    Submodular maximization generalizes many fundamental problems in discrete optimization, including Max-Cut in directed/undirected graphs, maximum coverage, maximum facility location and marketing over social networks. In this paper we consider the problem of maximizing any submodular function subject to $d$ knapsack constraints, where $d$ is a fixed constant. We establish a strong relation between the discrete problem and its continuous relaxation, obtained through {\\em extension by expectation} of the submodular function. Formally, we show that, for any non-negative submodular function, an $\\alpha$-approximation algorithm for the continuous relaxation implies a randomized $(\\alpha - \\eps)$-approximation algorithm for the discrete problem. We use this relation to improve the best known approximation ratio for the problem to $1/4- \\eps$, for any $\\eps > 0$, and to obtain a nearly optimal $(1-e^{-1}-\\eps)-$approximation ratio for the monotone case, for any $\\eps>0$. We further show that the probabilistic domain ...

  12. Difference Discrete Variational Principle in Discrete Mechanics and Symplectic Algorithm

    LUO Xu-Dong; GUO Han-Ying; LI Yu-Qi; WU Ke


    We propose the difference discrete variational principle in discrete mechanics and symplectic algorithmwith variable step-length of time in finite duration based upon a noncommutative differential calculus established inthis paper. This approach keeps both symplecticity and energy conservation discretely. We show that there exists thediscrete version of the Euler-Lagrange cohomology in these discrete systems. We also discuss the solution existencein finite time-length and its site density in continuous limit, and apply our approach to the pendulum with periodicperturbation. The numerical results are satisfactory.

  13. Discrete Exterior Calculus Discretization of Incompressible Navier-Stokes Equations

    Mohamed, Mamdouh S.


    A conservative discretization of incompressible Navier-Stokes equations over surface simplicial meshes is developed using discrete exterior calculus (DEC). Numerical experiments for flows over surfaces reveal a second order accuracy for the developed scheme when using structured-triangular meshes, and first order accuracy otherwise. The mimetic character of many of the DEC operators provides exact conservation of both mass and vorticity, in addition to superior kinetic energy conservation. The employment of barycentric Hodge star allows the discretization to admit arbitrary simplicial meshes. The discretization scheme is presented along with various numerical test cases demonstrating its main characteristics.

  14. Estimating the directions of arrival based on multi-subarray subspace fitting

    ZHU Weiqing; LIU Xiaodong; ZHANG Dongsheng; LIAO Zheng; ZHANG Fangsheng


    A multi-subarray subspace fitting method which take mutual coupling among array elements into account to estimate the directions of arrival was presented. The mutual coupling matrix of uniform linear arrays is modeled with a banded symmetric Toeplitz matrix. According to the DOF (Degree of Freedom) of mutual coupling matrix, part of the array elements at the two sides are neglected. The theoretical characteristics of ESPRIT algorithm, one of the subspace fitting algorithms, are studied. For large N and uniform linear array, the MCLS-ESPRIT estimation error is asymptotically jointly Gaussian distributed with zero means, and its covariance expression is obtained. It is known from the simulation that there exists a subarray with lowest estimation error for certain number of the array elements and the signal sources, and its performance of estimating the directions of arrival is close to the ideal situation when mutual coupling does not exist while its variance has some increase. The method has been applied to bathymetric sidescan sonar with high resolution, and good results have been obtained. At the cost of increasing the number of the array elements, the method can reduce the affection of the mutual coupling among array elements.

  15. Stochastic subspace identification for operational modal analysis of an arch bridge

    Loh, Chin-Hsiung; Chen, Ming-Che; Chao, Shu-Hsien


    In this paer the application of output-only system identification technique, known as Stochastic Subspace Identification (SSI) algorithms, for civil infrastructures is carried out. The ability of covariance driven stochastic subspace identification (SSI-COV) was proved through the analysis of the ambient data of an arch bridge under operational condition. A newly developed signal processing technique, Singular Spectrum analysis (SSA), capable to smooth noisy signals, is adopted for pre-processing the recorded data before the SSI. The conjunction of SSA and SSICOV provides a useful criterion for the system order determination. With the aim of estimating accurate modal parameters of the structure in off-line analysis, a stabilization diagram is constructed by plotting the identified poles of the system with increasing the size of data Hankel matrix. Identification task of a real structure, Guandu Bridge, is carried out to identify the system natural frequencies and mode shapes. The uncertainty of the identified model parameters from output-only measurement of the bridge under operation condition, such as temperature and traffic loading conditions, is discussed.

  16. Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

    Mathu Soothana S.Kumar Retna Swami; Muneeswaran Karuppiah


    An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis (PCA) and multiple discriminant analysis (MDA) is presented in this paper.In this framework,features are extracted from the optimal random image components using greedy approach.These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems.The design of Gabor filters,PCA and MDA are crucial processes used for facial feature extraction.The FERET,ORL and YALE face databases are used to generate the results.Experiments show that optimal random image component selection (ORICS) plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA.Our method achieves 96.25%,99.44% and 100% recognition accuracy on the FERET,ORL and YALE databases for 30% training respectively.This is a considerably improved performance compared with other standard methodologies described in the literature.

  17. Development of Subspace-based Hybrid Monte Carlo-Deterministric Algorithms for Reactor Physics Calculations

    Abdel-Khalik, Hany S. [North Carolina State Univ., Raleigh, NC (United States); Zhang, Qiong [North Carolina State Univ., Raleigh, NC (United States)


    The development of hybrid Monte-Carlo-Deterministic (MC-DT) approaches, taking place over the past few decades, have primarily focused on shielding and detection applications where the analysis requires a small number of responses, i.e. at the detector locations(s). This work further develops a recently introduced global variance reduction approach, denoted by the SUBSPACE approach is designed to allow the use of MC simulation, currently limited to benchmarking calculations, for routine engineering calculations. By way of demonstration, the SUBSPACE approach is applied to assembly level calculations used to generate the few-group homogenized cross-sections. These models are typically expensive and need to be executed in the order of 103 - 105 times to properly characterize the few-group cross-sections for downstream core-wide calculations. Applicability to k-eigenvalue core-wide models is also demonstrated in this work. Given the favorable results obtained in this work, we believe the applicability of the MC method for reactor analysis calculations could be realized in the near future.

  18. CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

    Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan


    In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

  19. On the Kalman Filter error covariance collapse into the unstable subspace

    A. Trevisan


    Full Text Available When the Extended Kalman Filter is applied to a chaotic system, the rank of the error covariance matrices, after a sufficiently large number of iterations, reduces to N+ + N0 where N+ and N0 are the number of positive and null Lyapunov exponents. This is due to the collapse into the unstable and neutral tangent subspace of the solution of the full Extended Kalman Filter. Therefore the solution is the same as the solution obtained by confining the assimilation to the space spanned by the Lyapunov vectors with non-negative Lyapunov exponents. Theoretical arguments and numerical verification are provided to show that the asymptotic state and covariance estimates of the full EKF and of its reduced form, with assimilation in the unstable and neutral subspace (EKF-AUS are the same. The consequences of these findings on applications of Kalman type Filters to chaotic models are discussed.

  20. Skin subspace color modeling for daytime and nighttime group activity recognition in confined operational spaces

    Shirkhodaie, Amir; Poshtyar, Azin; Chan, Alex; Hu, Shuowen


    In many military and homeland security persistent surveillance applications, accurate detection of different skin colors in varying observability and illumination conditions is a valuable capability for video analytics. One of those applications is In-Vehicle Group Activity (IVGA) recognition, in which significant changes in observability and illumination may occur during the course of a specific human group activity of interest. Most of the existing skin color detection algorithms, however, are unable to perform satisfactorily in confined operational spaces with partial observability and occultation, as well as under diverse and changing levels of illumination intensity, reflection, and diffraction. In this paper, we investigate the salient features of ten popular color spaces for skin subspace color modeling. More specifically, we examine the advantages and disadvantages of each of these color spaces, as well as the stability and suitability of their features in differentiating skin colors under various illumination conditions. The salient features of different color subspaces are methodically discussed and graphically presented. Furthermore, we present robust and adaptive algorithms for skin color detection based on this analysis. Through examples, we demonstrate the efficiency and effectiveness of these new color skin detection algorithms and discuss their applicability for skin detection in IVGA recognition applications.

  1. Dynamic Contrast-Enhanced MR Angiography Exploiting Subspace Projection for Robust Angiogram Separation.

    Park, Suhyung; Kim, Eung Yeop; Sohn, Chul-Ho; Park, Jaeseok


    Dynamic contrast-enhanced magnetic resonance angiography (DCE MRA) has been widely used as a clinical routine for diagnostic assessment of vascular morphology and hemodynamics. It requires high spatial and temporal resolution to capture rapid variation of DCE signals within a limited imaging time. Subtraction-based approaches are typically employed to selectively delineate arteries while eliminating unwanted background signals. Nevertheless, in the presence of subject motion with time, conventional subtraction approaches suffer from incomplete background suppression that impairs the detectability of arteries. In this work, we propose a novel, DCE MRA method that exploits subspace projection (SP) based angiogram separation for robust background suppression. A new, SP-based DCE signal model is introduced, in which images are decomposed into stationary background tissues, motion-induced artifacts, and DCE angiograms of interest. Constrained image reconstruction with sparsity priors is performed to project motion-induced artifacts onto the predefined subspace while extracting DCE angiograms of interest. Simulations and experimental studies validate that the proposed method outperforms existing techniques with increasing reduction factors in suppressing artifacts and noise.

  2. Variance analysis for model updating with a finite element based subspace fitting approach

    Gautier, Guillaume; Mevel, Laurent; Mencik, Jean-Mathieu; Serra, Roger; Döhler, Michael


    Recently, a subspace fitting approach has been proposed for vibration-based finite element model updating. The approach makes use of subspace-based system identification, where the extended observability matrix is estimated from vibration measurements. Finite element model updating is performed by correlating the model-based observability matrix with the estimated one, by using a single set of experimental data. Hence, the updated finite element model only reflects this single test case. However, estimates from vibration measurements are inherently exposed to uncertainty due to unknown excitation, measurement noise and finite data length. In this paper, a covariance estimation procedure for the updated model parameters is proposed, which propagates the data-related covariance to the updated model parameters by considering a first-order sensitivity analysis. In particular, this propagation is performed through each iteration step of the updating minimization problem, by taking into account the covariance between the updated parameters and the data-related quantities. Simulated vibration signals are used to demonstrate the accuracy and practicability of the derived expressions. Furthermore, an application is shown on experimental data of a beam.

  3. Large information plus noise random matrix models and consistent subspace estimation in large sensor networks

    Hachem, Walid; Mestre, Xavier; Najim, Jamal; Vallet, Pascal


    In array processing, a common problem is to estimate the angles of arrival of $K$ deterministic sources impinging on an array of $M$ antennas, from $N$ observations of the source signal, corrupted by gaussian noise. The problem reduces to estimate a quadratic form (called "localization function") of a certain projection matrix related to the source signal empirical covariance matrix. Recently, a new subspace estimation method (called "G-MUSIC") has been proposed, in the context where the number of available samples $N$ is of the same order of magnitude than the number of sensors $M$. In this context, the traditional subspace methods tend to fail because the empirical covariance matrix of the observations is a poor estimate of the source signal covariance matrix. The G-MUSIC method is based on a new consistent estimator of the localization function in the regime where $M$ and $N$ tend to $+\\infty$ at the same rate. However, the consistency of the angles estimator was not adressed. The purpose of this paper is ...

  4. Parallel algorithms for unconstrained optimization by multisplitting with inexact subspace search - the abstract

    Renaut, R.; He, Q. [Arizona State Univ., Tempe, AZ (United States)


    In a new parallel iterative algorithm for unconstrained optimization by multisplitting is proposed. In this algorithm the original problem is split into a set of small optimization subproblems which are solved using well known sequential algorithms. These algorithms are iterative in nature, e.g. DFP variable metric method. Here the authors use sequential algorithms based on an inexact subspace search, which is an extension to the usual idea of an inexact fine search. Essentially the idea of the inexact line search for nonlinear minimization is that at each iteration the authors only find an approximate minimum in the line search direction. Hence by inexact subspace search, they mean that, instead of finding the minimum of the subproblem at each interation, they do an incomplete down hill search to give an approximate minimum. Some convergence and numerical results for this algorithm will be presented. Further, the original theory will be generalized to the situation with a singular Hessian. Applications for nonlinear least squares problems will be presented. Experimental results will be presented for implementations on an Intel iPSC/860 Hypercube with 64 nodes as well as on the Intel Paragon.

  5. Reweighted mass center based object-oriented sparse subspace clustering for hyperspectral images

    Zhai, Han; Zhang, Hongyan; Zhang, Liangpei; Li, Pingxiang


    Considering the inevitable obstacles faced by the pixel-based clustering methods, such as salt-and-pepper noise, high computational complexity, and the lack of spatial information, a reweighted mass center based object-oriented sparse subspace clustering (RMC-OOSSC) algorithm for hyperspectral images (HSIs) is proposed. First, the mean-shift segmentation method is utilized to oversegment the HSI to obtain meaningful objects. Second, a distance reweighted mass center learning model is presented to extract the representative and discriminative features for each object. Third, assuming that all the objects are sampled from a union of subspaces, it is natural to apply the SSC algorithm to the HSI. Faced with the high correlation among the hyperspectral objects, a weighting scheme is adopted to ensure that the highly correlated objects are preferred in the procedure of sparse representation, to reduce the representation errors. Two widely used hyperspectral datasets were utilized to test the performance of the proposed RMC-OOSSC algorithm, obtaining high clustering accuracies (overall accuracy) of 71.98% and 89.57%, respectively. The experimental results show that the proposed method clearly improves the clustering performance with respect to the other state-of-the-art clustering methods, and it significantly reduces the computational time.

  6. High Resolution DOA Estimation Using Unwrapped Phase Information of MUSIC-Based Noise Subspace

    Ichige, Koichi; Saito, Kazuhiko; Arai, Hiroyuki

    This paper presents a high resolution Direction-Of-Arrival (DOA) estimation method using unwrapped phase information of MUSIC-based noise subspace. Superresolution DOA estimation methods such as MUSIC, Root-MUSIC and ESPRIT methods are paid great attention because of their brilliant properties in estimating DOAs of incident signals. Those methods achieve high accuracy in estimating DOAs in a good propagation environment, but would fail to estimate DOAs in severe environments like low Signal-to-Noise Ratio (SNR), small number of snapshots, or when incident waves are coming from close angles. In MUSIC method, its spectrum is calculated based on the absolute value of the inner product between array response and noise eigenvectors, means that MUSIC employs only the amplitude characteristics and does not use any phase characteristics. Recalling that phase characteristics plays an important role in signal and image processing, we expect that DOA estimation accuracy could be further improved using phase information in addition to MUSIC spectrum. This paper develops a procedure to obtain an accurate spectrum for DOA estimation using unwrapped and differentiated phase information of MUSIC-based noise subspace. Performance of the proposed method is evaluated through computer simulation in comparison with some conventional estimation methods.

  7. Poisson hierarchy of discrete strings

    Ioannidou, Theodora, E-mail: [Faculty of Civil Engineering, School of Engineering, Aristotle University of Thessaloniki, 54249, Thessaloniki (Greece); Niemi, Antti J., E-mail: [Department of Physics and Astronomy, Uppsala University, P.O. Box 803, S-75108, Uppsala (Sweden); Laboratoire de Mathematiques et Physique Theorique CNRS UMR 6083, Fédération Denis Poisson, Université de Tours, Parc de Grandmont, F37200, Tours (France); Department of Physics, Beijing Institute of Technology, Haidian District, Beijing 100081 (China)


    The Poisson geometry of a discrete string in three dimensional Euclidean space is investigated. For this the Frenet frames are converted into a spinorial representation, the discrete spinor Frenet equation is interpreted in terms of a transfer matrix formalism, and Poisson brackets are introduced in terms of the spinor components. The construction is then generalised, in a self-similar manner, into an infinite hierarchy of Poisson algebras. As an example, the classical Virasoro (Witt) algebra that determines reparametrisation diffeomorphism along a continuous string, is identified as a particular sub-algebra, in the hierarchy of the discrete string Poisson algebra. - Highlights: • Witt (classical Virasoro) algebra is derived in the case of discrete string. • Infinite dimensional hierarchy of Poisson bracket algebras is constructed for discrete strings. • Spinor representation of discrete Frenet equations is developed.

  8. Advances in discrete differential geometry


    This is one of the first books on a newly emerging field of discrete differential geometry and an excellent way to access this exciting area. It surveys the fascinating connections between discrete models in differential geometry and complex analysis, integrable systems and applications in computer graphics. The authors take a closer look at discrete models in differential geometry and dynamical systems. Their curves are polygonal, surfaces are made from triangles and quadrilaterals, and time is discrete. Nevertheless, the difference between the corresponding smooth curves, surfaces and classical dynamical systems with continuous time can hardly be seen. This is the paradigm of structure-preserving discretizations. Current advances in this field are stimulated to a large extent by its relevance for computer graphics and mathematical physics. This book is written by specialists working together on a common research project. It is about differential geometry and dynamical systems, smooth and discrete theories, ...

  9. Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions: Survey and Analysis

    Søren Holdt Jensen


    Full Text Available We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both diagonal (eigenvalue and singular value decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV, and ULLIV. In addition, we show how the subspace-based algorithms can be analyzed and compared by means of simple FIR filter interpretations. The algorithms are illustrated with working Matlab code and applications in speech processing.

  10. Subspace projection method for unstructured searches with noisy quantum oracles using a signal-based quantum emulation device

    La Cour, Brian R.; Ostrove, Corey I.


    This paper describes a novel approach to solving unstructured search problems using a classical, signal-based emulation of a quantum computer. The classical nature of the representation allows one to perform subspace projections in addition to the usual unitary gate operations. Although bandwidth requirements will limit the scale of problems that can be solved by this method, it can nevertheless provide a significant computational advantage for problems of limited size. In particular, we find that, for the same number of noisy oracle calls, the proposed subspace projection method provides a higher probability of success for finding a solution than does an single application of Grover's algorithm on the same device.

  11. Discrete R Symmetries and Anomalies

    Michael Dine(Santa Cruz Institute for Particle Physics and Department of Physics, Santa Cruz CA 95064, U.S.A.); Angelo Monteux(Santa Cruz Institute for Particle Physics, University of California Santa Cruz, 1156 High Street, Santa Cruz, U.S.A.)


    We comment on aspects of discrete anomaly conditions focussing particularly on $R$ symmetries. We review the Green-Schwarz cancellation of discrete anomalies, providing a heuristic explanation why, in the heterotic string, only the "model-independent dilaton" transforms non-linearly under discrete symmetries; this argument suggests that, in other theories, multiple fields might play a role in anomaly cancellations, further weakening any anomaly constraints at low energies. We provide examples...

  12. Representation of discrete Steklov-Poincare operator arising in domain decomposition methods in wavelet basis

    Jemcov, A.; Matovic, M.D. [Queen`s Univ., Kingston, Ontario (Canada)


    This paper examines the sparse representation and preconditioning of a discrete Steklov-Poincare operator which arises in domain decomposition methods. A non-overlapping domain decomposition method is applied to a second order self-adjoint elliptic operator (Poisson equation), with homogeneous boundary conditions, as a model problem. It is shown that the discrete Steklov-Poincare operator allows sparse representation with a bounded condition number in wavelet basis if the transformation is followed by thresholding and resealing. These two steps combined enable the effective use of Krylov subspace methods as an iterative solution procedure for the system of linear equations. Finding the solution of an interface problem in domain decomposition methods, known as a Schur complement problem, has been shown to be equivalent to the discrete form of Steklov-Poincare operator. A common way to obtain Schur complement matrix is by ordering the matrix of discrete differential operator in subdomain node groups then block eliminating interface nodes. The result is a dense matrix which corresponds to the interface problem. This is equivalent to reducing the original problem to several smaller differential problems and one boundary integral equation problem for the subdomain interface.

  13. Steerable Discrete Cosine Transform

    Fracastoro, Giulia; Fosson, Sophie M.; Magli, Enrico


    In image compression, classical block-based separable transforms tend to be inefficient when image blocks contain arbitrarily shaped discontinuities. For this reason, transforms incorporating directional information are an appealing alternative. In this paper, we propose a new approach to this problem, namely a discrete cosine transform (DCT) that can be steered in any chosen direction. Such transform, called steerable DCT (SDCT), allows to rotate in a flexible way pairs of basis vectors, and enables precise matching of directionality in each image block, achieving improved coding efficiency. The optimal rotation angles for SDCT can be represented as solution of a suitable rate-distortion (RD) problem. We propose iterative methods to search such solution, and we develop a fully fledged image encoder to practically compare our techniques with other competing transforms. Analytical and numerical results prove that SDCT outperforms both DCT and state-of-the-art directional transforms.

  14. Discrete Thermodynamics of Lasers

    Zilbergleyt, B


    The paper offers a discrete thermodynamic model of lasers. Laser is an open system; its equilibrium is based on a balance of two thermodynamic forces, one related to the incoming pumping power and another to the emitted light. The basic expression for such equilibrium is a logistic map, graphical solutions to which are pitchfork bifurcation diagrams. As pumping force increases, the relative populations on the ground and lasing branches tend to zero and unity correspondingly. An interesting feature of this model is the line spectrum of the up and down transitions between the branches beyond bifurcation point. Even in a simple case of 2-level laser with only 2 possible transition types (up and down), the spectra look like sets of the line packets, starting well before the population inversion. This effect is an independent confirmation of the Einstein's prohibition on practical realization of 2-level laser. Multilevel lasers may be approached by employing the idea of thermodynamic activity for the emitting atom...

  15. Discrete anti-gravity

    Noyes, H. Pierre; Starson, Scott


    Discrete physics, because it replaces time evolution generated by the energy operator with a global bit-string generator (program universe) and replaces fields with the relativistic Wheeler-Feynman action at a distance, allows the consistent formulation of the concept of signed gravitational charge for massive particles. The resulting prediction made by this version of the theory is that free anti-particles near the surface of the earth will fall up with the same acceleration that the corresponding particles fall down. So far as we can see, no current experimental information is in conflict with this prediction of our theory. The experiment crusis will be one of the anti-proton or anti-hydrogen experiments at CERN. Our prediction should be much easier to test than the small effects which those experiments are currently designed to detect or bound.

  16. Discrete anti-gravity

    Noyes, H.P. (Stanford Linear Accelerator Center, Menlo Park, CA (USA)); Starson, S. (STARSON Corp. (USA))


    Discrete physics, because it replaces time evolution generated by the energy operator with a global bit-string generator (program universe) and replaces fields'' with the relativistic Wheeler-Feynman action at a distance,'' allows the consistent formulation of the concept of signed gravitational charge for massive particles. The resulting prediction made by this version of the theory is that free anti-particles near the surface of the earth will fall'' up with the same acceleration that the corresponding particles fall down. So far as we can see, no current experimental information is in conflict with this prediction of our theory. The experiment crusis will be one of the anti-proton or anti-hydrogen experiments at CERN. Our prediction should be much easier to test than the small effects which those experiments are currently designed to detect or bound. 23 refs.

  17. Discrete Pearson distributions

    Bowman, K.O. [Oak Ridge National Lab., TN (United States); Shenton, L.R. [Georgia Univ., Athens, GA (United States); Kastenbaum, M.A. [Kastenbaum (M.A.), Basye, VA (United States)


    These distributions are generated by a first order recursive scheme which equates the ratio of successive probabilities to the ratio of two corresponding quadratics. The use of a linearized form of this model will produce equations in the unknowns matched by an appropriate set of moments (assumed to exist). Given the moments we may find valid solutions. These are two cases; (1) distributions defined on the non-negative integers (finite or infinite) and (2) distributions defined on negative integers as well. For (1), given the first four moments, it is possible to set this up as equations of finite or infinite degree in the probability of a zero occurrence, the sth component being a product of s ratios of linear forms in this probability in general. For (2) the equation for the zero probability is purely linear but may involve slowly converging series; here a particular case is the discrete normal. Regions of validity are being studied. 11 refs.

  18. Immigration and Prosecutorial Discretion.

    Apollonio, Dorie; Lochner, Todd; Heddens, Myriah

    Immigration has become an increasingly salient national issue in the US, and the Department of Justice recently increased federal efforts to prosecute immigration offenses. This shift, however, relies on the cooperation of US attorneys and their assistants. Traditionally federal prosecutors have enjoyed enormous discretion and have been responsive to local concerns. To consider how the centralized goal of immigration enforcement may have influenced federal prosecutors in regional offices, we review their prosecution of immigration offenses in California using over a decade's worth of data. Our findings suggest that although centralizing forces influence immigration prosecutions, individual US attorneys' offices retain distinct characteristics. Local factors influence federal prosecutors' behavior in different ways depending on the office. Contrary to expectations, unemployment rates did not affect prosecutors' willingness to pursue immigration offenses, nor did local popular opinion about illegal immigration.

  19. A discrete, finite multiverse

    McKenzie, Alan


    The Many Worlds Interpretation (MWI) famously avoids the issue of wave function collapse. Different MWI trees representing the same quantum events can have different topologies, depending upon the observer. However, they are all isomorphic to the group of block universes containing all of the outcomes of all of the events, and so, in that sense, the group of block universes is a more fundamental representation. Different branches of the MWI tree, representing different universes in MWI, ultimately share the same quantum state in a common ancestor branch. This branching topology is incompatible with that of the Minkowski block universe; the resolution is to replace the branches with discrete, parallel block universes, each of which extends from the trunk to the outermost twigs. The number of universes in a branch is proportional to its thickness which, in turn, depends upon the absolute square of the probability amplitude for the state in that branch. Every quantum event may be represented by a kernel of unive...

  20. Thermodynamics of discrete quantum processes

    Anders, Janet; Giovannetti, Vittorio


    We define thermodynamic configurations and identify two primitives of discrete quantum processes between configurations for which heat and work can be defined in a natural way. This allows us to uncover a general second law for any discrete trajectory that consists of a sequence of these primitives, linking both equilibrium and non-equilibrium configurations. Moreover, in the limit of a discrete trajectory that passes through an infinite number of configurations, i.e. in the reversible limit, we recover the saturation of the second law. Finally, we show that for a discrete Carnot cycle operating between four configurations one recovers Carnot's thermal efficiency.

  1. Principles of discrete time mechanics

    Jaroszkiewicz, George


    Could time be discrete on some unimaginably small scale? Exploring the idea in depth, this unique introduction to discrete time mechanics systematically builds the theory up from scratch, beginning with the historical, physical and mathematical background to the chronon hypothesis. Covering classical and quantum discrete time mechanics, this book presents all the tools needed to formulate and develop applications of discrete time mechanics in a number of areas, including spreadsheet mechanics, classical and quantum register mechanics, and classical and quantum mechanics and field theories. A consistent emphasis on contextuality and the observer-system relationship is maintained throughout.

  2. Asymptotics of robust utility maximization

    Knispel, Thomas


    For a stochastic factor model we maximize the long-term growth rate of robust expected power utility with parameter $\\lambda\\in(0,1)$. Using duality methods the problem is reformulated as an infinite time horizon, risk-sensitive control problem. Our results characterize the optimal growth rate, an optimal long-term trading strategy and an asymptotic worst-case model in terms of an ergodic Bellman equation. With these results we propose a duality approach to a "robust large deviations" criterion for optimal long-term investment.

  3. Multivariate residues and maximal unitarity

    Søgaard, Mads; Zhang, Yang


    We extend the maximal unitarity method to amplitude contributions whose cuts define multidimensional algebraic varieties. The technique is valid to all orders and is explicitly demonstrated at three loops in gauge theories with any number of fermions and scalars in the adjoint representation. Deca-cuts realized by replacement of real slice integration contours by higher-dimensional tori encircling the global poles are used to factorize the planar triple box onto a product of trees. We apply computational algebraic geometry and multivariate complex analysis to derive unique projectors for all master integral coefficients and obtain compact analytic formulae in terms of tree-level data.

  4. Beeping a Maximal Independent Set

    Afek, Yehuda; Alon, Noga; Bar-Joseph, Ziv; Cornejo, Alejandro; Haeupler, Bernhard; Kuhn, Fabian


    We consider the problem of computing a maximal independent set (MIS) in an extremely harsh broadcast model that relies only on carrier sensing. The model consists of an anonymous broadcast network in which nodes have no knowledge about the topology of the network or even an upper bound on its size. Furthermore, it is assumed that an adversary chooses at which time slot each node wakes up. At each time slot a node can either beep, that is, emit a signal, or be silent. At a particular time slot...

  5. Maximal Congruences on Some Semigroups

    Jintana Sanwong; R.P. Sullivan


    In 1976 Howie proved that a finite congruence-free semigroup is a simple group if it has at least three elements but no zero elementInfinite congruence-free semigroups are far more complicated to describe, but some have been constructed using semigroups of transformations (for example, by Howie in 1981 and by Marques in 1983)Here, forcertain semigroups S of numbers and of transformations, we determine all congruences p on S such that S/p is congruence-free, that is, we describe all maximal congruences on such semigroups S.

  6. Discrete optimization in architecture extremely modular systems

    Zawidzki, Machi


    This book is comprised of two parts, both of which explore modular systems: Pipe-Z (PZ) and Truss-Z (TZ), respectively. It presents several methods of creating PZ and TZ structures subjected to discrete optimization. The algorithms presented employ graph-theoretic and heuristic methods. The underlying idea of both systems is to create free-form structures using the minimal number of types of modular elements. PZ is more conceptual, as it forms single-branch mathematical knots with a single type of module. Conversely, TZ is a skeletal system for creating free-form pedestrian ramps and ramp networks among any number of terminals in space. In physical space, TZ uses two types of modules that are mirror reflections of each other. The optimization criteria discussed include: the minimal number of units, maximal adherence to the given guide paths, etc.

  7. Knowledge discovery by accuracy maximization.

    Cacciatore, Stefano; Luchinat, Claudio; Tenori, Leonardo


    Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data. Unlike other data mining methods, the peculiarity of KODAMA is that it is driven by an integrated procedure of cross-validation of the results. The discovery of a local manifold's topology is led by a classifier through a Monte Carlo procedure of maximization of cross-validated predictive accuracy. Briefly, our approach differs from previous methods in that it has an integrated procedure of validation of the results. In this way, the method ensures the highest robustness of the obtained solution. This robustness is demonstrated on experimental datasets of gene expression and metabolomics, where KODAMA compares favorably with other existing feature extraction methods. KODAMA is then applied to an astronomical dataset, revealing unexpected features. Interesting and not easily predictable features are also found in the analysis of the State of the Union speeches by American presidents: KODAMA reveals an abrupt linguistic transition sharply separating all post-Reagan from all pre-Reagan speeches. The transition occurs during Reagan's presidency and not from its beginning.

  8. Inapproximability of maximal strip recovery

    Jiang, Minghui


    In comparative genomic, the first step of sequence analysis is usually to decompose two or more genomes into syntenic blocks that are segments of homologous chromosomes. For the reliable recovery of syntenic blocks, noise and ambiguities in the genomic maps need to be removed first. Maximal Strip Recovery (MSR) is an optimization problem proposed by Zheng, Zhu, and Sankoff for reliably recovering syntenic blocks from genomic maps in the midst of noise and ambiguities. Given $d$ genomic maps as sequences of gene markers, the objective of \\msr{d} is to find $d$ subsequences, one subsequence of each genomic map, such that the total length of syntenic blocks in these subsequences is maximized. For any constant $d \\ge 2$, a polynomial-time 2d-approximation for \\msr{d} was previously known. In this paper, we show that for any $d \\ge 2$, \\msr{d} is APX-hard, even for the most basic version of the problem in which all gene markers are distinct and appear in positive orientation in each genomic map. Moreover, we provi...

  9. Maximal right smooth extension chains

    Huang, Yun Bao


    If $w=u\\alpha$ for $\\alpha\\in \\Sigma=\\{1,2\\}$ and $u\\in \\Sigma^*$, then $w$ is said to be a \\textit{simple right extension}of $u$ and denoted by $u\\prec w$. Let $k$ be a positive integer and $P^k(\\epsilon)$ denote the set of all $C^\\infty$-words of height $k$. Set $u_{1},\\,u_{2},..., u_{m}\\in P^{k}(\\epsilon)$, if $u_{1}\\prec u_{2}\\prec ...\\prec u_{m}$ and there is no element $v$ of $P^{k}(\\epsilon)$ such that $v\\prec u_{1}\\text{or} u_{m}\\prec v$, then $u_{1}\\prec u_{2}\\prec...\\prec u_{m}$ is said to be a \\textit{maximal right smooth extension (MRSE) chains}of height $k$. In this paper, we show that \\textit{MRSE} chains of height $k$ constitutes a partition of smooth words of height $k$ and give the formula of the number of \\textit{MRSE} chains of height $k$ for each positive integer $k$. Moreover, since there exist the minimal height $h_1$ and maximal height $h_2$ of smooth words of length $n$ for each positive integer $n$, we find that \\textit{MRSE} chains of heights $h_1-1$ and $h_2+1$ are good candidates t...

  10. Discrete dynamics versus analytic dynamics

    Toxværd, Søren


    For discrete classical Molecular dynamics obtained by the “Verlet” algorithm (VA) with the time increment h there exists a shadow Hamiltonian H˜ with energy E˜(h) , for which the discrete particle positions lie on the analytic trajectories for H˜ . Here, we proof that there, independent...

  11. Discretization error of Stochastic Integrals

    Fukasawa, Masaaki


    Asymptotic error distribution for approximation of a stochastic integral with respect to continuous semimartingale by Riemann sum with general stochastic partition is studied. Effective discretization schemes of which asymptotic conditional mean-squared error attains a lower bound are constructed. Two applications are given; efficient delta hedging strategies with transaction costs and effective discretization schemes for the Euler-Maruyama approximation are constructed.

  12. Discrete Mathematics and Its Applications

    Oxley, Alan


    The article gives ideas that lecturers of undergraduate Discrete Mathematics courses can use in order to make the subject more interesting for students and encourage them to undertake further studies in the subject. It is possible to teach Discrete Mathematics with little or no reference to computing. However, students are more likely to be…

  13. Subspace Dimensionality: A Tool for Automated QC in Seismic Array Processing

    Rowe, C. A.; Stead, R. J.; Begnaud, M. L.


    Because of the great resolving power of seismic arrays, the application of automated processing to array data is critically important in treaty verification work. A significant problem in array analysis is the inclusion of bad sensor channels in the beamforming process. We are testing an approach to automated, on-the-fly quality control (QC) to aid in the identification of poorly performing sensor channels prior to beam-forming in routine event detection or location processing. The idea stems from methods used for large computer servers, when monitoring traffic at enormous numbers of nodes is impractical on a node-by node basis, so the dimensionality of the node traffic is instead monitoried for anomalies that could represent malware, cyber-attacks or other problems. The technique relies upon the use of subspace dimensionality or principal components of the overall system traffic. The subspace technique is not new to seismology, but its most common application has been limited to comparing waveforms to an a priori collection of templates for detecting highly similar events in a swarm or seismic cluster. In the established template application, a detector functions in a manner analogous to waveform cross-correlation, applying a statistical test to assess the similarity of the incoming data stream to known templates for events of interest. In our approach, we seek not to detect matching signals, but instead, we examine the signal subspace dimensionality in much the same way that the method addresses node traffic anomalies in large computer systems. Signal anomalies recorded on seismic arrays affect the dimensional structure of the array-wide time-series. We have shown previously that this observation is useful in identifying real seismic events, either by looking at the raw signal or derivatives thereof (entropy, kurtosis), but here we explore the effects of malfunctioning channels on the dimension of the data and its derivatives, and how to leverage this effect for

  14. Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring

    Caicedo, Alexander; Varon, Carolina; Hunyadi, Borbala; Papademetriou, Maria; Tachtsidis, Ilias; Van Huffel, Sabine


    Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the

  15. EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

    Ashari, Rehab Bahaaddin

    Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only

  16. Discretization and implicit mapping dynamics

    Luo, Albert C J


    This unique book presents the discretization of continuous systems and implicit mapping dynamics of periodic motions to chaos in continuous nonlinear systems. The stability and bifurcation theory of fixed points in discrete nonlinear dynamical systems is reviewed, and the explicit and implicit maps of continuous dynamical systems are developed through the single-step and multi-step discretizations. The implicit dynamics of period-m solutions in discrete nonlinear systems are discussed. The book also offers a generalized approach to finding analytical and numerical solutions of stable and unstable periodic flows to chaos in nonlinear systems with/without time-delay. The bifurcation trees of periodic motions to chaos in the Duffing oscillator are shown as a sample problem, while the discrete Fourier series of periodic motions and chaos are also presented. The book offers a valuable resource for university students, professors, researchers and engineers in the fields of applied mathematics, physics, mechanics,...

  17. A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data.

    Pehlevan, Cengiz; Hu, Tao; Chklovskii, Dmitri B


    Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis, by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function, these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a principled cost function. Here, to bridge this gap, we derive a biologically plausible network for subspace learning on streaming data by minimizing a principled cost function. In a departure from previous work, where cost was quantified by the representation, or reconstruction, error, we adopt a multidimensional scaling cost function for streaming data. The resulting algorithm relies only on biologically plausible Hebbian and anti-Hebbian local learning rules. In a stochastic setting, synaptic weights converge to a stationary state, which projects the input data onto the principal subspace. If the data are generated by a nonstationary distribution, the network can track the principal subspace. Thus, our result makes a step toward an algorithmic theory of neural computation.

  18. The maximal D = 4 supergravities

    Wit, Bernard de [Institute for Theoretical Physics and Spinoza Institute, Utrecht University, Postbus 80.195, NL-3508 TD Utrecht (Netherlands); Samtleben, Henning [Laboratoire de Physique, ENS Lyon, 46 allee d' Italie, F-69364 Lyon CEDEX 07 (France); Trigiante, Mario [Dept. of Physics, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Turin (Italy)


    All maximal supergravities in four space-time dimensions are presented. The ungauged Lagrangians can be encoded in an E{sub 7(7)}-Sp(56; R)/GL(28) matrix associated with the freedom of performing electric/magnetic duality transformations. The gauging is defined in terms of an embedding tensor {theta} which encodes the subgroup of E{sub 7(7)} that is realized as a local invariance. This embedding tensor may imply the presence of magnetic charges which require corresponding dual gauge fields. The latter can be incorporated by using a recently proposed formulation that involves tensor gauge fields in the adjoint representation of E{sub 7(7)}. In this formulation the results take a universal form irrespective of the electric/magnetic duality basis. We present the general class of supersymmetric and gauge invariant Lagrangians and discuss a number of applications.

  19. Maximizing profit using recommender systems

    Das, Aparna; Ricketts, Daniel


    Traditional recommendation systems make recommendations based solely on the customer's past purchases, product ratings and demographic data without considering the profitability the items being recommended. In this work we study the question of how a vendor can directly incorporate the profitability of items into its recommender so as to maximize its expected profit while still providing accurate recommendations. Our approach uses the output of any traditional recommender system and adjust them according to item profitabilities. Our approach is parameterized so the vendor can control how much the recommendation incorporating profits can deviate from the traditional recommendation. We study our approach under two settings and show that it achieves approximately 22% more profit than traditional recommendations.

  20. The maximal D=5 supergravities

    de Wit, Bernard; Trigiante, M; Wit, Bernard de; Samtleben, Henning; Trigiante, Mario


    The general Lagrangian for maximal supergravity in five spacetime dimensions is presented with vector potentials in the \\bar{27} and tensor fields in the 27 representation of E_6. This novel tensor-vector system is subject to an intricate set of gauge transformations, describing 3(27-t) massless helicity degrees of freedom for the vector fields and 3t massive spin degrees of freedom for the tensor fields, where the (even) value of t depends on the gauging. The kinetic term of the tensor fields is accompanied by a unique Chern-Simons coupling which involves both vector and tensor fields. The Lagrangians are completely encoded in terms of the embedding tensor which defines the E_6 subgroup that is gauged by the vectors. The embedding tensor is subject to two constraints which ensure the consistency of the combined vector-tensor gauge transformations and the supersymmetry of the full Lagrangian. This new formulation encompasses all possible gaugings.

  1. Constraint Propagation as Information Maximization

    Abdallah, A Nait


    Dana Scott used the partial order among partial functions for his mathematical model of recursively defined functions. He interpreted the partial order as one of information content. In this paper we elaborate on Scott's suggestion of regarding computation as a process of information maximization by applying it to the solution of constraint satisfaction problems. Here the method of constraint propagation can be interpreted as decreasing uncertainty about the solution -- that is, as gain in information about the solution. As illustrative example we choose numerical constraint satisfaction problems to be solved by interval constraints. To facilitate this approach to constraint solving we formulate constraint satisfaction problems as formulas in predicate logic. This necessitates extending the usual semantics for predicate logic so that meaning is assigned not only to sentences but also to formulas with free variables.

  2. Budget Allocation for Maximizing Viral Advertising in Social Networks

    Bo-Lei Zhang; Zhu-Zhong Qian; Wen-Zhong Li; Bin Tang; Xiaoming Fu


    Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via social links to become viral. Despite extensive researches in how to target the most influential users, a key issue is often neglected: how to incentivize the initial adopters. In the problem of influence maximization, the assumption is that each user has a fixed cost for being initial adopters, while in practice, user decisions for accepting the budget to be initial adopters are often probabilistic rather than deterministic. In this paper, we study optimal budget allocation in social networks to maximize the spread of viral advertising. In particular, a concave probability model is introduced to characterize each user’s utility for being an initial adopter. Under this model, we show that it is NP-hard to find an optimal budget allocation for maximizing the spread of viral advertising. We then present a novel discrete greedy algorithm with near optimal performance, and further propose scaling-up techniques to improve the time-efficiency of our algorithm. Extensive experiments on real-world social graphs are implemented to validate the effectiveness of our algorithm in practice. The results show that our algorithm can outperform other intuitive heuristics significantly in almost all cases.

  3. Efficient Structural System Reliability Updating with Subspace-Based Damage Detection Information

    Döhler, Michael; Thöns, Sebastian

    modelling is introduced building upon the non-destructive testing reliability which applies to structural systems and DDS containing a strategy to overcome the high computational efforts for the pre-determination of the DDS reliability. This approach takes basis in the subspace-based damage detection method......Damage detection systems and algorithms (DDS and DDA) provide information of the structural system integrity in contrast to e.g. local information by inspections or non-destructive testing techniques. However, the potential of utilizing DDS information for the structural integrity assessment...... and prognosis is hardly exploited nor treated in scientific literature up to now. In order to utilize the information provided by DDS for the structural performance, usually high computational efforts for the pre-determination of DDS reliability are required. In this paper, an approach for the DDS performance...

  4. Short-recurrence Krylov subspace methods for the overlap Dirac operator at nonzero chemical potential

    Bloch, Jacques C R; Frommer, Andreas; Heybrock, Simon; Schaefer, Katrin; Wettig, Tilo


    The overlap operator in lattice QCD requires the computation of the sign function of a matrix, which is non-Hermitian in the presence of a quark chemical potential. In previous work we introduced an Arnoldi-based Krylov subspace approximation, which uses long recurrences. Even after the deflation of critical eigenvalues, the low efficiency of the method restricts its application to small lattices. Here we propose new short-recurrence methods which strongly enhance the efficiency of the computational method. Using rational approximations to the sign function we introduce two variants, based on the restarted Arnoldi process and on the two-sided Lanczos method, respectively, which become very efficient when combined with multishift solvers. Alternatively, in the variant based on the two-sided Lanczos method the sign function can be evaluated directly. We present numerical results which compare the efficiencies of a restarted Arnoldi-based method and the direct two-sided Lanczos approximation for various lattice ...

  5. Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

    Siddiqui, Bilal A.


    In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

  6. Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes.

    Al-Jabery, Khalid; Obafemi-Ajayi, Tayo; Olbricht, Gayla R; Takahashi, T Nicole; Kanne, Stephen; Wunsch, Donald


    Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.

  7. Decentralized system identification using stochastic subspace identification for wireless sensor networks.

    Cho, Soojin; Park, Jong-Woong; Sim, Sung-Han


    Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.

  8. Initial Results in Power System Identification from Injected Probing Signals Using a Subspace Method

    Zhou, Ning; Pierre, John W.; Hauer, John F.


    In this paper, the authors use the Numerical algorithm for Subspace State Space System IDentification (N4SID) to extract dynamic parameters from phasor measurements collected on the western North American Power Grid. The data were obtained during tests on June 7, 2000, and they represent wide area response to several kinds of probing signals including Low-Level Pseudo-Random Noise (LLPRN) and Single-Mode Square Wave (SMSW) injected at the Celilo terminal of the Pacific HVDC In-tertie (PDCI). An identified model is validated using a cross vali-dation method. Also, the obtained electromechanical modes are compared with the results from Prony analysis of a ringdown and with signal analysis of ambient data measured under similar op-erating conditions. The consistent results show that methods in this class can be highly effective even when the probing signal is small.

  9. [Classification technique for hyperspectral image based on subspace of bands feature extraction and LS-SVM].

    Gao, Heng-zhen; Wan, Jian-wei; Zhu, Zhen-zhen; Wang, Li-bao; Nian, Yong-jian


    The present paper proposes a novel hyperspectral image classification algorithm based on LS-SVM (least squares support vector machine). The LS-SVM uses the features extracted from subspace of bands (SOB). The maximum noise fraction (MNF) method is adopted as the feature extraction method. The spectral correlations of the hyperspectral image are used in order to divide the feature space into several SOBs. Then the MNF is used to extract characteristic features of the SOBs. The extracted features are combined into the feature vector for classification. So the strong bands correlation is avoided and the spectral redundancies are reduced. The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved. The proposed method optimizes spectral information by feature extraction and reduces the spectral noise. The classifier performance is improved. Experimental results show the superiorities of the proposed algorithm.

  10. Quantum computation with prethreshold superconducting qubits: Single-excitation subspace approach

    Galiautdinov, Andrei


    We describe an alternative approach to quantum computation that is ideally suited for today's sub-threshold-fidelity qubits, and which can be applied to a family of hardware models that includes superconducting qubits with tunable coupling. In this approach, the computation on an n-qubit processor is carried out in the n-dimensional single-excitation subspace (SES) of the full 2^n-dimensional Hilbert space. Because any real Hamiltonian can be directly generated in the SES [E. J. Pritchett et al., arXiv:1008.0701], high-dimensional unitary operations can be carried out in a single step, bypassing the need to decompose into single- and two-qubit gates. Although technically nonscalable and unsuitable for applications (including Shor's) requiring enormous Hilbert spaces, this approach would make practical a first-generation quantum computer capable of achieving significant quantum speedup.

  11. Joint DOA and multi-pitch estimation based on subspace techniques

    Xi Zhang, Johan; Christensen, Mads Græsbøll; Jensen, Søren Holdt; Moonen, Marc


    In this article, we present a novel method for high-resolution joint direction-of-arrivals (DOA) and multi-pitch estimation based on subspaces decomposed from a spatio-temporal data model. The resulting estimator is termed multi-channel harmonic MUSIC (MC-HMUSIC). It is capable of resolving sources under adverse conditions, unlike traditional methods, for example when multiple sources are impinging on the array from approximately the same angle or similar pitches. The effectiveness of the method is demonstrated on a simulated an-echoic array recordings with source signals from real recorded speech and clarinet. Furthermore, statistical evaluation with synthetic signals shows the increased robustness in DOA and fundamental frequency estimation, as compared with to a state-of-the-art reference method.

  12. A new damage diagnosis approach for NC machine tools based on hybrid Stationary subspace analysis

    Gao, Chen; Zhou, Yuqing; Ren, Yan


    This paper focused on the damage diagnosis for NC machine tools and put forward a damage diagnosis method based on hybrid Stationary subspace analysis (SSA), for improving the accuracy and visibility of damage identification. First, the observed single sensor signal was reconstructed to multi-dimensional signals by the phase space reconstruction technique, as the inputs of SSA. SSA method was introduced to separate the reconstructed data into stationary components and non-stationary components without the need for independency and prior information of the origin signals. Subsequently, the selected non-stationary components were analysed for training LS-SVM (Least Squares Support Vector Machine) classifier model, in which several statistic parameters in the time and frequency domains were exacted as the sample of LS-SVM. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy of the proposed approach.

  13. Quantum computation in decoherence-free subspace via cavity-decay-assisted adiabatic passage

    FENG Xunli


    Full Text Available In this work,a scheme for quantum computation based on cavity QED in a decoherence-free subspaces via using the technique of stimulated Raman adiabatic passage (STIRAP is proposed.To implement universal quantum logic gates that form basic blocks of quantum computation,we suppose two atoms are trapped in a single-mode cavity with large decay rates and are driven by the laser fields.The relatively large cavity decay can be used for the continuous detection of the cavity mode as so-called ″cavity-decay-induced quantum Zeno effect″.The results show that,decoherence induced by the atomic spontaneous emission and cavity decay can be efficiently suppress with the STIRAP technique and the quantum Zeno effect.

  14. Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays

    Xin Zhang


    Full Text Available Small aperture microphone arrays provide many advantages for portable devices and hearing aid equipment. In this paper, a subspace based localization method is proposed for acoustic source using small aperture arrays. The effects of array aperture on localization are analyzed by using array response (array manifold. Besides array aperture, the frequency of acoustic source and the variance of signal power are simulated to demonstrate how to optimize localization performance, which is carried out by introducing frequency error with the proposed method. The proposed method for 5 mm array aperture is validated by simulations and experiments with MEMS microphone arrays. Different types of acoustic sources can be localized with the highest precision of 6 degrees even in the presence of wind noise and other noises. Furthermore, the proposed method reduces the computational complexity compared with other methods.

  15. Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing

    Majid Shakhsi Dastgahian


    Full Text Available Millimeter-wave communication (mmWC is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS and mobile sets (MS. Unlike the conventional MIMO systems, Millimeter-wave (mmW systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.

  16. Bayesian estimation of Karhunen-Loève expansions; A random subspace approach

    Chowdhary, Kenny; Najm, Habib N.


    One of the most widely-used procedures for dimensionality reduction of high dimensional data is Principal Component Analysis (PCA). More broadly, low-dimensional stochastic representation of random fields with finite variance is provided via the well known Karhunen-Loève expansion (KLE). The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L2 sense, i.e., which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition) on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build probabilistic Karhunen-Loève expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.

  17. Positive circuits and maximal number of fixed points in discrete dynamical systems

    Richard, Adrien


    The biologist Ren\\'e Thomas conjectured, twenty years ago, that the presence of a positive circuit in the interaction graph of a dynamical system is a necessary condition for the presence of multiple stable states. Recently, Richard and Comet stated and proved this conjecture for systems whose dynamics is described by the iterations of a map $F$ which operates on the product $X$ of $n$ finite intervals of integers. In this paper, we widely extend the result of Richard and Comet by exhibiting an upper bound on the number of fixed points for $F$ which only depends on $X$ and on the topology of the positive circuits of the interaction graph associated with $F$. Our motivation for this work comes from biology. The maps $F$ we focus on are indeed extensively used to described the behavior of gene regulatory networks, and when such a network is studied, the first reliable data often concern its interaction graph, so that the bound we establish can be used, in practice, to obtain valuable information about the numbe...

  18. Discrete Darboux transformation for discrete polynomials of hypergeometric type

    Bangerezako, Gaspard


    The Darboux transformation, well known in second-order differential operator theory, is applied to the difference equations satisfied by the discrete hypergeometric polynomials (Charlier, Meixner-Kravchuk, Hahn).

  19. On invariant ellipsoids for discrete-time systems by saturated optimal controls

    Bin ZHOU; Guangren DUAN


    Analytical approximation of the maximal invariant ellipsoid for discrete-time linear systems with saturated optimal control is established, which is less conservative than existing computationally un-intensive results. Simultaneously, necessary and sufficient conditions for such approximation being equal to the real maximal invariant ellipsoid is presented.All results are given analytically and can easily be implemented in practice.An illustrative example is given to show the effectiveness of the proposed approach.

  20. The origin of discrete particles

    Bastin, T


    This book is a unique summary of the results of a long research project undertaken by the authors on discreteness in modern physics. In contrast with the usual expectation that discreteness is the result of mathematical tools for insertion into a continuous theory, this more basic treatment builds up the world from the discrimination of discrete entities. This gives an algebraic structure in which certain fixed numbers arise. As such, one agrees with the measured value of the fine-structure constant to one part in 10,000,000 (10 7 ). Sample Chapter(s). Foreword (56 KB). Chapter 1: Introduction