A new family of life distributions for dependent data: Estimation

One of the biggest problems in reliability analysis is determining an appropriate distribution of life data. Therefore, this paper develops the estimation aspect of a family of life distributions obtained from spherical distributions. Additionally, a new family of life distributions is proposed for dependent life data, together with an optimization algorithm based on the simulated annealing method. This algorithm is very efficient for optimization purposes and does not require any manipulation of the log-likelihood functions for the distributions proposed in this study.     

A new family of life distributions for dependent data: Estimation

One of the biggest problems in reliability analysis is determining an appropriate distribution of life data. Therefore, this paper develops the estimation aspect of a family of life distributions obtained from spherical distributions. Additionally, a new family of life distributions is proposed for dependent life data, together with an optimization algorithm based on the simulated annealing method. This algorithm is very efficient for optimization purposes and does not require any manipulation of the log-likelihood functions for the distributions proposed in this study.     

Nonlinear model order reduction with low rank tensor approximation

In this article, two methods of model order reduction based on the low rank approximation of tensor are introduced for the large scale nonlinear problem. We first introduce some definitions and results on tensor extended from matrix theory. Then we show how the general nonlinear system can be converted into the low rank form we treated in this research. We put the model order reduction of it in two frameworks, that is, polynomial framework and moment‐matching framework. In these two frameworks we construct the algorithms correspondingly, and analyze properties of these algorithms, including the preservation of stability, and moment‐matching properties. Next the priorities of these algorithms are presented. Finally we setup several numerical experiments to validate the effectiveness of the algorithms.     

Tensor rank one differential graph preserving analysis for facial expression recognition

This paper presents a new dimensionality reduction algorithm for multi-dimensional data based on the tensor rank-one decomposition and graph preserving criterion. Through finding proper rank-one tensors, the algorithm effectively enhances the pairwise inter-class margins and meanwhile preserves the intra-class local manifold structure. In the algorithm, a novel marginal neighboring graph is devised to describe the pairwise inter-class boundaries, and a differential formed objective function is adopted to ensure convergence. Furthermore, the algorithm has less computation in comparison with the vector representation based and the tensor-to-tensor projection based algorithms. The experiments for the basic facial expressions recognition show its effectiveness, especially when it is followed by a neural network classifier.     

Functional signatures:new definition and constructions

Functional signatures (FS) enable a master authority to delegate its signing privilege to an assistant.Concretely,the master authority uses its secret key skF t...     

A new definition of SLDNF-resolution

We propose a new, “top-down” definition of SLDNF-resolution that retains the spirit of the original definition, but avoids the difficulties noted in the literature. We compare it with the “bottom-up” definition of Kunen [7].     

Hierarchical tensor approximation of multi-dimensional visual data

Visual data comprise of multi-scale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional dataset is transformed into a hierarchy of signals to expose its multi-scale structures. The signal at each level of the hierarchy is further divided into a number of smaller tensors to expose its spatially inhomogeneous structures. These smaller tensors are further transformed and pruned using a tensor approximation technique. Our hierarchical tensor approximation supports progressive transmission and partial decompression. Experimental results indicate that our technique can achieve higher compression ratios and quality than previous methods, including wavelet transforms, wavelet packet transforms, and single-level tensor approximation. We have successfully applied our technique to multiple tasks involving multi-dimensional visual data, including medical and scientific data visualization, data-driven rendering and texture synthesis.     

Low-rank tensor completion with fractional-Jacobian-extended tensor regularization for multi-component visual data inpainting

Several low-rank tensor completion methods have been integrated with total variation (TV) regularization to retain edge information and promote piecewise smoothness. In this paper, we first construct a fractional Jacobian matrix to nonlocally couple the structural correlations across components and propose a fractional-Jacobian-extended tensor regularization model, whose energy functional was designed proportional to the mixed norm of the fractional Jacobian matrix. Consistent regularization could thereby be performed on each component, avoiding band-by-band TV regularization and enabling effective handling of the contaminated fine-grained and complex details due to the introduction of a fractional differential. Since the proposed spatial regularization is linear convex, we further produced a novel fractional generalization of the classical primal-dual resolvent to develop its solver efficiently. We then combined the proposed tensor regularization model with low-rank constraints for tensor completion and addressed the problem by employing the augmented Lagrange multiplier method, which provides a splitting scheme. Several experiments were conducted to illustrate the performance of the proposed method for RGB and multispectral image restoration, especially its abilities to recover complex structures and the details of multi-component visual data effectively.     

A new definition of shape similarity

Two reference points of a region are defined which do not depend on the position, size and orientation of the region. Reference points are used to get borders on the basis of which the shape distance and shape similarity are defined.     

Equilibrium-independent passivity: A new definition and numerical certification

We extend the traditional notion of passivity to a forced system whose equilibrium is dependent on the control input by defining equilibrium-independent passivity, a system property characterized by a dissipation inequality centered at an arbitrary equilibrium point. We provide a necessary input/output condition which can be tested for systems of arbitrary dimension and sufficient conditions to certify this property for scalar systems. An example from network stability analysis is presented which demonstrates the utility of this new definition. We then proceed to numerical certification of equilibrium-independent passivity using sum-of-squares programming. Finally, through numerical examples we show that equilibrium-independent passivity is less restrictive than incremental passivity.     

Computing rank dependent utility in graphical models for sequential decision problems

This paper is devoted to automated sequential decision in AI. More precisely, we focus here on the Rank Dependent Utility (RDU) model. This model is able to encompass rational decision behaviors that the Expected Utility model cannot accommodate. However, the non-linearity of RDU makes it difficult to compute an RDU-optimal strategy in sequential decision problems. This has considerably slowed the use of RDU in operational contexts. In this paper, we are interested in providing new algorithmic solutions to compute an RDU-optimal strategy in graphical models. Specifically, we present algorithms for solving decision tree models and influence diagram models of sequential decision problems. For decision tree models, we propose a mixed integer programming formulation that is valid for a subclass of RDU models (corresponding to risk seeking behaviors). This formulation reduces to a linear program when mixed strategies are considered. In the general case (i.e., when there is no particular assumption on the parameters of RDU), we propose a branch and bound procedure to compute an RDU-optimal strategy among the pure ones. After highlighting the difficulties induced by the use of RDU in influence diagram models, we show how this latter procedure can be extended to optimize RDU in an influence diagram. Finally, we provide empirical evaluations of all the presented algorithms.     

Multiple rank multi-linear SVM for matrix data classification

Matrices, or more generally, multi-way arrays (tensors) are common forms of data that are encountered in a wide range of real applications. How to classify this kind of data is an important research topic for both pattern recognition and machine learning. In this paper, by analyzing the relationship between two famous traditional classification approaches, i.e., SVM and STM, a novel tensor-based method, i.e., multiple rank multi-linear SVM (MRMLSVM), is proposed. Different from traditional vector-based and tensor based methods, multiple-rank left and right projecting vectors are employed to construct decision boundary and establish margin function. We reveal that the rank of transformation can be regarded as a tradeoff parameter to balance the capacity of learning and generalization in essence. We also proposed an effective approach to solve the proposed non-convex optimization problem. The convergence behavior, initialization, computational complexity and parameter determination problems are analyzed. Compared with vector-based classification methods, MRMLSVM achieves higher accuracy and has lower computational complexity. Compared with traditional supervised tensor-based methods, MRMLSVM performs better for matrix data classification. Promising experimental results on various kinds of data sets are provided to show the effectiveness of our method.     

Association patterns for data modeling and definition

Association patterns provide guidance for modeling the associations that occur among objects within both the real world and the solution domains of computer applications. The patterns help the designer better understand and more precisely define the semantics of these associations, which allows them to be more easily and properly implemented. This paper describes a number of association patterns using Object Relationship Notation (ORN) and by doing so provides evidence for the effectiveness of this notation. It also shows how the development of database systems can be improved by an approach that uses association patterns to build a database model and then implements the model by mapping it to an ORN-extended database definition that is supported by a DBMS. The feasibility of this approach and the applicability of our association patterns have been validated by DBMS research prototypes and by the modeling, implementing, and testing of numerous associations.     

Design and implementation of data abstraction definition facility

This paper describes the Implementation model of a data definition facility for abstract data types, implemented as an extension to PL/1. The facility is based on a modified version of the cluster mechanism for the implementation of types. The proposed version tries to address the issues of efficiency and portability in connection with the goal of systematic programming.     

A New Definition of the Hough Transform

This paper‘s main contributions are three-fold.Firstly,it is shown that the two existing template matching-like definitions of the Hough transform in the literature are inadequate.Secondly,an inherent probabilistic aspect of the Hough transform embedded in the transformation process from image space to parameter space is clarified.Thirdly,a new definition of the Hough transform is proposed which takes into account both the intersection scheme between the mapping curve(or mapping surface) and accumulator cells and the inherent probabilistic characteristics.     

Chasing real-time interaction in new media: towards a new theoretical approach and definition

‘Real-Time’ is one of the most frequent concepts used referring to digital media. Despite it is commonly related to immediate feedback, it is not clear when it happens and what its characteristics are, especially in current digital interfaces. Although real-time has been written about very extensively in the context of Human Computer Interaction and Computational Systems, there are few studies evaluating the key features of real-time interaction in other domains. This article attempts to address this situation by proposing a new approach to the concept of real-time interaction driving the analysis of this topic into paradigmatic cases studies in new media, video games and digital artworks. This new analysis reveals several specific facets that allow us to identify new factors in the understanding of this fuzzy concept. Finally, the article proposes a new definition of ‘real-time interaction’ based on the findings in the context of new media practices.     

Using bilingual Web data to mine and rank translations

The English Reading Wizard uses bilingual Web and local-dictionary data to help readers understand foreign languages by translating words and phrases. Methods include the expectation maximization algorithm and bilingual bootstrapping.     

A survey of multilinear subspace learning for tensor data

Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract useful information from these massive data. This paper surveys the field of multilinear subspace learning (MSL) for dimensionality reduction of multidimensional data directly from their tensorial representations. It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining the algorithmic aspects of typical MSL solutions, and categorizing both unsupervised and supervised MSL algorithms into taxonomies. Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions.