Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition

A new representation of spatio-temporal random processes is proposed in this work. In practical applications, such processes are used to model velocity fields, temperature distributions, response of vibrating systems, to name a few. Finding an efficient representation for any random process leads to encapsulation of information which makes it more convenient for a practical implementations, for instance, in a computational mechanics problem. For a single-parameter process such as spatial or temporal process, the eigenvalue decomposition of the covariance matrix leads to the well-known Karhunen-Loeve (KL) decomposition. However, for multiparameter processes such as a spatio-temporal process, the covariance function itself can be defined in multiple ways. Here the process is assumed to be measured at a finite set of spatial locations and a finite number of time instants. Then the spatial covariance matrix at different time instants are considered to define the covariance of the process. This set of square, symmetric, positive semi-definite matrices is then represented as a third-order tensor. A suitable decomposition of this tensor can identify the dominant components of the process, and these components are then used to define a closed-form representation of the process. The procedure is analogous to the KL decomposition for a single-parameter process, however, the decompositions and interpretations vary significantly. The tensor decompositions are successfully applied on (i) a heat conduction problem, (ii) a vibration problem, and (iii) a covariance function taken from the literature that was fitted to model a measured wind velocity data. It is observed that the proposed representation provides an efficient approximation to some processes. Furthermore, a comparison with KL decomposition showed that the proposed method is computationally cheaper than the KL, both in terms of computer memory and execution time.

Finite size scaling in crossover among different random matrix ensembles in microscopic lattice models

Using numerical diagonalization we study the crossover among different random matrix ensembles (Poissonian, Gaussian orthogonal ensemble (GOE), Gaussian unitary ensemble (GUE) and Gaussian symplectic ensemble (GSE)) realized in two different microscopic models. The specific diagnostic tool used to study the crossovers is the level spacing distribution. The first model is a one-dimensional lattice model of interacting hard-core bosons (or equivalently spin 1/2 objects) and the other a higher dimensional model of non-interacting particles with disorder and spin-orbit coupling. We find that the perturbation causing the crossover among the different ensembles scales to zero with system size as a power law with an exponent that depends on the ensembles between which the crossover takes place. This exponent is independent of microscopic details of the perturbation. We also find that the crossover from the Poissonian ensemble to the other three is dominated by the Poissonian to GOE crossover which introduces level repulsion while the crossover from GOE to GUE or GOE to GSE associated with symmetry breaking introduces a subdominant contribution. We also conjecture that the exponent is dependent on whether the system contains interactions among the elementary degrees of freedom or not and is independent of the dimensionality of the system.

Matrix-Assisted Refolding, Purification and Activity Assessment Using a `Form Invariant' Assay for Matrix Metalloproteinase 2 (MMP2)

Matrix metalloproteinases expression is used as biomarker for various cancers and associated malignancies. Since these proteinases can cleave many intracellular proteins, overexpression tends to be toxic; hence, a challenge to purify them. To overcome these limitations, we designed a protocol where full length pro-MMP2 enzyme was overexpressed in E. coli as inclusion bodies and purified using 6xHis affinity chromatography under denaturing conditions. In one step, the enzyme was purified and refolded directly on the affinity matrix under redox conditions to obtain a bioactive protein. The pro-MMP2 protein was characterized by mass spectrometry, CD spectroscopy, zymography and activity analysis using a simple in-house developed `form invariant' assay, which reports the total MMP2 activity independent of its various forms. The methodology yielded higher yields of bioactive protein compared to other strategies reported till date, and we anticipate that using the protocol, other toxic proteins can also be overexpressed and purified from E. coli and subsequently refolded into active form using a one step renaturation protocol.

Polynomial Decompositions in Polynomial Time

Fix a prime p. Given a positive integer k, a vector of positive integers Delta = (Delta(1), Delta(2), ... , Delta(k)) and a function Gamma : F-p(k) -> F-p, we say that a function P : F-p(n) -> F-p is (k, Delta, Gamma)-structured if there exist polynomials P-1, P-2, ..., P-k : F-p(n) -> F-p with each deg(P-i) <= Delta(i) such that for all x is an element of F-p(n), P(x) = Gamma(P-1(x), P-2(x), ..., P-k(x)). For instance, an n-variate polynomial over the field Fp of total degree d factors nontrivially exactly when it is (2, (d - 1, d - 1), prod)- structured where prod(a, b) = a . b. We show that if p > d, then for any fixed k, Delta, Gamma, we can decide whether a given polynomial P(x(1), x(2), ..., x(n)) of degree d is (k, Delta, Gamma)-structured and if so, find a witnessing decomposition. The algorithm takes poly(n) time. Our approach is based on higher-order Fourier analysis.

SnO2 nanowire anchored graphene nanosheet matrix for the superior performance of Li-ion thin film battery anode

All solid state batteries are essential candidate for miniaturizing the portable electronics devices. Thin film batteries are constructed by layer by layer deposition of electrode materials by physical vapour deposition method. We propose a promising novel method and unique architecture, in which highly porous graphene sheet embedded with SnO2 nanowire could be employed as the anode electrode in lithium ion thin film battery. The vertically standing graphene flakes were synthesized by microwave plasma CVD and SnO2 nanowires based on a vapour-liquid-solid (VLS) mechanism via thermal evaporation at low synthesis temperature (620 degrees C). The graphene sheet/SnO2 nanowire composite electrode demonstrated stable cycling behaviours and delivered a initial high specific discharge capacity of 1335 mAh g(-1) and 900 mAh g(-1) after the 50th cycle. Furthermore, the SnO2 nanowire electrode displayed superior rate capabilities with various current densities.

OPTIMALITY OF THE PRODUCT-MATRIX CONSTRUCTION FOR SECURE MSR REGENERATING CODES

In this paper, we consider the security of exact-repair regenerating codes operating at the minimum-storage-regenerating (MSR) point. The security requirement (introduced in Shah et. al.) is that no information about the stored data file must be leaked in the presence of an eavesdropper who has access to the contents of l(1) nodes as well as all the repair traffic entering a second disjoint set of l(2) nodes. We derive an upper bound on the size of a data file that can be securely stored that holds whenever l(2) <= d - k +1. This upper bound proves the optimality of the product-matrix-based construction of secure MSR regenerating codes by Shah et. al.

Matrix Crack Detection in Composite Plate with Spatially Random Material Properties using Fractal Dimension

Fractal dimension based damage detection method is investigated for a composite plate with random material properties. Composite material shows spatially varying random material properties because of complex manufacturing processes. Matrix cracks are considered as damage in the composite plate. Such cracks are often seen as the initial damage mechanism in composites under fatigue loading and also occur due to low velocity impact. Static deflection of the cantilevered composite plate with uniform loading is calculated using the finite element method. Damage detection is carried out based on sliding window fractal dimension operator using the static deflection. Two dimensional homogeneous Gaussian random field is generated using Karhunen-Loeve (KL) expansion to represent the spatial variation of composite material property. The robustness of fractal dimension based damage detection method is demonstrated considering the composite material properties as a two dimensional random field.

Matrix crack detection in spatially random composite structures using fractal dimension

Fractal dimension based damage detection method is studied for a composite structure with random material properties. A composite plate with localized matrix crack is considered. Matrix cracks are often seen as the initial damage mechanism in composites. Fractal dimension based method is applied to the static deformation curve of the structure to detect localized damage. Static deflection of a cantilevered composite plate under uniform loading is calculated using the finite element method. Composite material shows spatially varying random material properties because of complex manufacturing processes. Spatial variation of material property is represented as a two dimensional homogeneous Gaussian random field. Karhunen-Loeve (KL) expansion is used to generate a random field. The robustness of fractal dimension based damage detection methods is studied considering the composite plate with spatial variation in material properties.

Low-Wear High-Friction Behavior of Copper Matrix Composites Dispersed With an In Situ Polymer Derived Ceramic

We show that copper-matrix composites that contain 20 vol. % of an in situ processed, polymer-derived, ceramic phase constituted from Si-C-N have unusual friction-and-wear properties. They show negligible wear despite a coefficient of friction (COF) that approaches 0.7. This behavior is ascribed to the lamellar structure of the composite such that the interlamellar regions are infused with nanoscale dispersion of ceramic particles. There is significant hardening of the composite just adjacent to the wear surface by severe plastic deformation.

A matrix model for QCD: QCD color is mixed

We use general arguments to show that colored QCD states when restricted to gauge invariant local observables are mixed. This result has important implications for confinement: a pure colorless state can never evolve into two colored states by unitary evolution. Furthermore, the mean energy in such a mixed colored state is infinite. Our arguments are confirmed in a matrix model for QCD that we have developed using the work of Narasimhan and Ramadas(3) and Singer.(2) This model, a (0 + 1)-dimensional quantum mechanical model for gluons free of divergences and capturing important topological aspects of QCD, is adapted to analytical and numerical work. It is also suitable to work on large N QCD. As applications, we show that the gluon spectrum is gapped and also estimate some low-lying levels for N = 2 and 3 (colors). Incidentally the considerations here are generic and apply to any non-Abelian gauge theory.

A matrix model for QCD

Gribov's observation that global gauge fixing is impossible has led to suggestions that there may be a deep connection between gauge fixing and confinement. We find an unexpected relation between the topological nontriviality of the gauge bundle and colored states in SU(N) Yang-Mills theory, and show that such states are necessarily impure. We approximate QCD by a rectangular matrix model that captures the essential topological features of the gauge bundle, and demonstrate the impure nature of colored states explicitly. Our matrix model also allows the inclusion of the QCD theta-term, as well as to perform explicit computations of low-lying glueball masses. This mass spectrum is gapped. Since an impure state cannot evolve to a pure one by a unitary transformation, our result shows that the solution to the confinement problem in pure QCD is fundamentally quantum information-theoretic.

Unified Continuum Damage Model for Matrix Cracking in Composite Rotor Blades

This paper deals with modeling of the first damage mode, matrix micro-cracking, in helicopter rotor/wind turbine blades and how this effects the overall cross-sectional stiffness. The helicopter/wind turbine rotor system operates in a highly dynamic and unsteady environment leading to severe vibratory loads present in the system. Repeated exposure to this loading condition can induce damage in the composite rotor blades. These rotor/turbine blades are generally made of fiber-reinforced laminated composites and exhibit various competing modes of damage such as matrix micro-cracking, delamination, and fiber breakage. There is a need to study the behavior of the composite rotor system under various key damage modes in composite materials for developing Structural Health Monitoring (SHM) system. Each blade is modeled as a beam based on geometrically non-linear 3-D elasticity theory. Each blade thus splits into 2-D analyzes of cross-sections and non-linear 1-D analyzes along the beam reference curves. Two different tools are used here for complete 3-D analysis: VABS for 2-D cross-sectional analysis and GEBT for 1-D beam analysis. The physically-based failure models for matrix in compression and tension loading are used in the present work. Matrix cracking is detected using two failure criterion: Matrix Failure in Compression and Matrix Failure in Tension which are based on the recovered field. A strain variable is set which drives the damage variable for matrix cracking and this damage variable is used to estimate the reduced cross-sectional stiffness. The matrix micro-cracking is performed in two different approaches: (i) Element-wise, and (ii) Node-wise. The procedure presented in this paper is implemented in VABS as matrix micro-cracking modeling module. Three examples are presented to investigate the matrix failure model which illustrate the effect of matrix cracking on cross-sectional stiffness by varying the applied cyclic

Novel architecture for anomalous strengthening of a particulate filled polymer matrix composite

We propose an architecture for dramatically enhancing the stress bearing and energy absorption capacities of a polymer based composite. Different weight fractions of iron oxide nano-particles (NPs) are mixed in a poly(dimethylesiloxane) (PDMS) matrix either uniformly or into several vertically aligned cylindrical pillars. These composites are compressed up to a strain of 60% at a strain rate of 0.01 s(-1) following which they are fully unloaded at the same rate. Load bearing and energy absorption capacities of the composite with uniform distribution of NPs increase by similar to 50% upon addition of 5 wt% of NPs; however, these properties monotonically decrease with further addition of NPs so much so that the load bearing capacity of the composite becomes 1/6th of PDMS upon addition of 20 wt% of NPs. On the contrary, stress at a strain of 60% and energy absorption capacity of the composites with pillar configuration monotonically increase with the weight fraction of NPs in the pillars wherein the load bearing capacity becomes 1.5 times of PDMS when the pillars consisted of 20 wt% of NPs. In situ mechanical testing of composites with pillars reveals outward bending of the pillars wherein the pillars and the PDMS in between two pillars, located along a radius, are significantly compressed. Reasoning based on effects of compressive hydrostatic stress and shape of fillers is developed to explain the observed anomalous strengthening of the composite with pillar architecture.

Carbon-Nanohorn-Reinforced Polymer Matrix Composites: Synergetic Benefits in Mechanical Properties

Mechanical properties of single-walled carbon nanohoms (SWNH) and SWNH plus few-layer graphene (EG)-reinforced poly(vinyl alcohol) (PVA) matrix composites have been measured using the nanoindentation technique. The elastic modulus (E) and hardness (H) of PVA were found to improve by similar to 315% and similar to 135%, respectively, upon the addition of just 0.4 wt % SWNH. These properties were found to be comparable to those obtained upon the addition of 0.2 wt % single-walled nanotubes (SWNT) to PVA. Furthermore, upon binary addition of 0.2 wt % EG and 0.4 wt % SWNH to PVA, benefits in the form of similar to 400% and similar to 330% synergy in E and H, respectively, were observed, along with an increased resistance to viscoelastic deformation. The reasons for these improvements are discussed in terms of the dimensionality of nanocarbon, the effectiveness of nanocarbon and polymer matrix interaction, and the influence of nanocarbon on the degree of crystallinity of the polymer. The results from SWNH reinforcement in this study demonstrate the scope for a novel and, in contrast to SWNT composites, a commercially feasible opportunity for strengthening polymer matrices.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.