Confident Kernel Sparse Coding and Dictionary Learning

In recent years, kernel-based sparse coding (K-SRC) has received particular attention due to its efficient representation of nonlinear data structures in the feature space. Nevertheless, the existing K-SRC methods suffer from the lack of consistency between their training and test optimization frameworks. In this work, we propose a novel confident K-SRC and dictionary learning algorithm (CKSC) which focuses on the discriminative reconstruction of the data based on its representation in the kernel space. CKSC focuses on reconstructing each data sample via weighted contributions which are confident in its corresponding class of data. We employ novel discriminative terms to apply this scheme to both training and test frameworks in our algorithm. This specific design increases the consistency of these optimization frameworks and improves the discriminative performance in the recall phase. In addition, CKSC directly employs the supervised information in its dictionary learning framework to enhance the discriminative structure of the dictionary. For empirical evaluations, we implement our CKSC algorithm on multivariate time-series benchmarks such as DynTex++ and UTKinect. Our claims regarding the superior performance of the proposed algorithm are justified throughout comparing its classification results to the state-of-the-art K-SRC algorithms.Comment: 10 pages, ICDM 2018 conferenc

Isogeometric analysis of Cahn-Hilliard phase field-based Binary-Fluid-Structure Interaction based on an ALE variational formulation

This thesis is concerned with the development of a computational model and simulation technique capable of capturing the complex physics behind the intriguing phenomena of Elasto-capillarity. Elastocapillarity refers to the ability of capillary forces or surface tensions to deform elastic solids through a complex interplay between the energy of the surfaces (interfaces) and the elastic strain energy in the solid bulk. The described configuration gives rise to a three-phase system featuring a fluid-fluid interface (for instance the interface of a liquid and an ambient fluid such as air) and two additional interfaces separating the elastic solid from the first and second fluids, respectively. This setup is encountered in the wetting of soft substrates which is an emerging young field of research with many potential applications in micro- and nanotechnology and biomechanics. By virtue of the fact that a lot of physical phenomena under the umbrella of the wetting of soft substrates (e.g. Stick-slip motion, Durotaxis, Shuttleworth effect, etc.) are not yet fully understood, numerical analysis and simulation tools may yield invaluable insights when it comes to understanding the underlying processes. The problem tackled in this work – dubbed Elasto-Capillary Fluid-Structure Interaction or Binary-Fluid-Structure Interaction (BFSI) – is of multiphysics nature and poses a tremendous and challenging complexity when it comes to its numerical treatment. The complexity is given by the individual difficulties of the involved Two-phase Flow and Fluid-Structure Interaction (FSI) subproblems and the additional complexity emerging from their complex interplay. The two-phase flow problems considered in this work are immiscible two-component incompressible flow problems which we address with a Cahn-Hilliard phase field-based two-phase flow model through the Navier-Stokes-Cahn-Hilliard (NSCH) equations. The phase field method – also known as the diffuse interface method – is based on models of fluid free energy and has a solid theoretical foundation in thermodynamics and statistical mechanics. It may therefore be perceived as a physically motivated extension of the level-set or volume-of-fluid methods. It differs from other Eulerian interface motion techniques by virtue of the fact that it does not feature a sharp, but a diffuse interface of finite width whose dynamics are governed by the joint minimization of a double well chemical energy and a gradientsquared surface energy – both being constituents of the fluid free energy. Particularly for two-phase flows, diffuse interface models have gained a lot of attention due to their ability to handle complex interface dynamics such moving contact lines on wetted surfaces, and droplet coalescence or segregation without any special procedures. Our computational model for the FSI subproblem is based on a hyperelastic material model for the solid. When modeling the coupled dynamics of FSI, one is confronted with the dilemma that the fluid model is naturally based on an Eulerian perspective while it is very natural to express the solid problem in Lagrangian formulation. The monolithic approach we take, uses a fully coupled Arbitrary Lagrangian– Eulerian (ALE) variational formulation of the FSI problem and applies Galerkin-based Isogeometric Analysis for the discretization of the partial differential equations involved. This approach solves the difficulty of a common variational description and facilitates a consistent Galerkin discretization of the FSI problem. Besides, the monolithic approach avoids any instability issues that are associated with partitioned FSI approaches when the fluid and solid densities approach each other. The BFSI computational model presented in this work is obtained through the combination of the above described phase field-based two-phase flow and the monolithic fluid-structure interaction models and yields a very robust and powerful method for the simulation of elasto-capillary fluid-structure interaction problems. In addition, we also show that it may also be used for the modeling of FSI with free surfaces, involving totally or partially submerged solids. Our BFSI model may be classified as “quasi monolithic” as we employ a two-step solution algorithm, where in the first step we solve the pure Cahn-Hilliard phase field problem and use its results in a second step in which the binary-fluid-flow, the solid deformation and the mesh regularization problems are solved monolithically

Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201

New bounds on the signed total domination number of graphs

In this paper, we study the signed total domination number in graphs and present new sharp lower and upper bounds for this parameter. For example by making use of the classic theorem of Turan, we present a sharp lower bound on this parameter for graphs with no complete graph of order r+1 as a subgraph. Also, we prove that n-2(s-s') is an upper bound on the signed total domination number of any tree of order n with s support vertices and s' support vertives of degree two. Moreover, we characterize all trees attainig this bound.Comment: This paper contains 11 pages and one figur

Stochastic First-Order Learning for Large-Scale Flexibly Tied Gaussian Mixture Model

Gaussian Mixture Models (GMM) are one of the most potent parametric density estimators based on the kernel model that finds application in many scientific domains. In recent years, with the dramatic enlargement of data sources, typical machine learning algorithms, e.g. Expectation Maximization (EM), encounters difficulty with high-dimensional and streaming data. Moreover, complicated densities often demand a large number of Gaussian components. This paper proposes a fast online parameter estimation algorithm for GMM by using first-order stochastic optimization. This approach provides a framework to cope with the challenges of GMM when faced with high-dimensional streaming data and complex densities by leveraging the flexibly-tied factorization of the covariance matrix. A new stochastic Manifold optimization algorithm that preserves the orthogonality is introduced and used along with the well-known Euclidean space numerical optimization. Numerous empirical results on both synthetic and real datasets justify the effectiveness of our proposed stochastic method over EM-based methods in the sense of better-converged maximum for likelihood function, fewer number of needed epochs for convergence, and less time consumption per epoch

An investigation on relationship between CRM and organizational learning through knowledge management: A case study of Tehran travel agency

Customer relationship management (CRM) plays essential role on the success of many business units. CRM integrates necessary data from internal and external sources to assist managers and employees for business development. This paper attempts to analyze relationship between CRM, organizational learning, and knowledge management. Research population includes travel agencies in Tehran, Iran and their manager are considered for the purpose of this study. This research has four variables 1- Successful implementation of KM, 2- Organizational learning, 3- customer orientation, and 4- information share with customers. The preliminary results of this survey indicate that any development of CRM will significantly contribute relative efficiency of this travel agency. The results also indicate that there is a meaningful relationship among components of CRM including organizational learning, and knowledge management in this travel agency

A Lagrangean Relaxation Approach for the Modular Hub Location Problem

Hub location problems deal with the location of hub facilities and the allocation of the demand nodes to hub facilities so as to effectively route the demand between origin–destination pairs. Transportation systems such as mail, freight, passenger and even telecommunication systems most often employ hub and spoke networks to provide a strong balance between high service quality and low costs resulting in an economically competitive operation. In this study the Modular Hub Location Problem (Multiple assignments without direct connections) (MHLP-MA) is introduced. A Lagrangean relaxation method is used to approximately solve large scale instances. It relaxes a set of complicating constraints to efficiently obtain lower and upper bounds on the optimal solution of the problem. Computational experiments are performed in order to evaluate the effectiveness and limitations of the proposed model and solution method