representation problem
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2020 ◽  
Vol 257 ◽  
pp. 105448
Author(s):  
Rashid A. Aliev ◽  
Vugar E. Ismailov

Author(s):  
Özge Gürsoy Atar

Women's television programs appear as an indispensable broadcasting component of the morning television broadcasting series. Three women's programs, which were broadcasted in 2018, were analyzed in the study of how women participate in these television programs and the presentation of gender roles. First, the issue of social representation in the context of the problem of representation in the media was discussed. Later on, feminism and women's programs in the media were examined and the situation in the media was determined. In the application part of the study, numerical data were evaluated, and discourse analysis was conducted within the framework of the women's programs examined.


2019 ◽  
Vol 268 ◽  
pp. 106919 ◽  
Author(s):  
Alessandro Caterino ◽  
Rita Ceppitelli ◽  
Ľubica Holá

Author(s):  
Max A. Little

Digital signal processing and machine learning require digital data which can be processed by algorithms on computer. However, most of the real-world signals that we observe are real numbers, occurring at real time values. This means that it is impossible in practice to store these signals on a computer and we must find some approximate signal representation which is amenable to finite, digital storage. This chapter describes the main methods which are used in practice to solve this representation problem.


2019 ◽  
Vol 19 (07) ◽  
pp. 2050129 ◽  
Author(s):  
Papri Dey

In this paper, we consider the problem of representing a multivariate polynomial as the determinant of a definite (monic) symmetric/Hermitian linear matrix polynomial (LMP). Such a polynomial is known as determinantal polynomial. Determinantal polynomials can characterize the feasible sets of semidefinite programming (SDP) problems that motivates us to deal with this problem. We introduce the notion of generalized mixed discriminant (GMD) of matrices which translates the determinantal representation problem into computing a point of a real variety of a specified ideal. We develop an algorithm to determine such a determinantal representation of a bivariate polynomial of degree [Formula: see text]. Then we propose a heuristic method to obtain a monic symmetric determinantal representation (MSDR) of a multivariate polynomial of degree [Formula: see text].


2018 ◽  
Vol 25 (5) ◽  
pp. 561-571 ◽  
Author(s):  
Ekaterina Khvorostukhina ◽  
Vladimir Molchanov

Hypergraphic automata are automata with state sets and input symbol sets being hypergraphs which are invariant under actions of transition and output functions. Universally attracting objects of a category of hypergraphic automata are automata Atm(H1,H2). Here,H1is a state hypergraph,H2is classified as an output symbol hypergraph, andS= EndH1× Hom(H1,H2) is an input symbol semigroup. Such automata are called universal hypergraphic automata. The input symbol semigroupSof such an automaton Atm(H1,H2) is an algebra of mappings for such an automaton. Semigroup properties are interconnected with properties of the algebraic structure of the automaton. Thus, we can study universal hypergraphic automata with the help of their input symbol semigroups. In this paper, we investigated a representation problem of universal hypergraphic automata in their input symbol semigroup. The main result of the current study describes a universal hypergraphic automaton as a multiple-set algebraic structure canonically constructed from autonomous input automaton symbols. Such a structure is one of the major tools for proving relatively elementary definability of considered universal hypergraphic automata in a class of semigroups in order to analyze interrelation of elementary characteristics of universal hypergraphic automata and their input symbol semigroups. The main result of the paper is the solution of this problem for universal hypergraphic automata for effective hypergraphs withp-definable edges. It is an important class of automata because such an algebraic structure variety includes automata with state sets and output symbol sets represented by projective or affine planes, along with automata with state sets and output symbol sets divided into equivalence classes. The article is published in the authors' wording.


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