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

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

Cellular Separation Forces Studied by Force Spectroscopy and Microplate Manipulation

During adaptive immune response, peptides from foreign microorganisms are processed and displayed on the surface of Antigen Presenting Cells (APCs) within the cleft of major histocompatibility complexes (pMHC), which are recognized by T cells that patrol the body. In recent years, a large spatially organized molecular cluster called immune synapse (IS) has been identifed, which forms at the T cell/APC interface. Little is known about the biophysics of these cell conjugates and it is generally believed that cell adhesion molecules are activated on the T cell surface after recognition, which presumably leads to higher adhesion forces between the two cells. Thus, the goal of this thesis was to measure the change of total adhesion forces between T cells and APCs when peptide was present compared to the case when peptide was absent. In this work, we report the first measurement of separation forces between T cells and APCs employing a cell stretcher device and Atomic Force Microscopy (AFM). We analyze the kinetics of T cells that interact with APCs presenting a foreign peptide by their MHC (pulsed), and APCs carrying no peptide (unpulsed) for contact times of 0 to 60 minutes. Unlike pulsed cells, unpulsed APCs did not show the formation of immune synapses. Forces are comparable for short time points but start to deviate after 15 min to reach a maximum after 30 min in the pulsed case. We were able to correlate single rupture events in AFM spectra to protein unbinding theories and, hence, gained a deeper insight into the molecular basis for the increase in adhesion forces. Thus, this work provides an important contribution to the physical understanding of cell-cell adhesion