Certain Types of Linear Codes over the Finite Field of Order Twenty-Five

The aim of the paper is to compute projective maximum distance separable codes, -MDS of two and three dimensions with certain lengths and Hamming weight distribution from the arcs in the projective line and plane over the finite field of order twenty-five. Also, the linear codes generated by an incidence matrix of points and lines of were studied over different finite fields.

Querying a Matrix through Matrix-Vector Products

We consider algorithms with access to an unknown matrix M ε F n×d via matrix-vector products , namely, the algorithm chooses vectors v 1 , ⃛ , v q , and observes Mv 1 , ⃛ , Mv q . Here the v i can be randomized as well as chosen adaptively as a function of Mv 1 , ⃛ , Mv i-1 . Motivated by applications of sketching in distributed computation, linear algebra, and streaming models, as well as connections to areas such as communication complexity and property testing, we initiate the study of the number q of queries needed to solve various fundamental problems. We study problems in three broad categories, including linear algebra, statistics problems, and graph problems. For example, we consider the number of queries required to approximate the rank, trace, maximum eigenvalue, and norms of a matrix M; to compute the AND/OR/Parity of each column or row of M, to decide whether there are identical columns or rows in M or whether M is symmetric, diagonal, or unitary; or to compute whether a graph defined by M is connected or triangle-free. We also show separations for algorithms that are allowed to obtain matrix-vector products only by querying vectors on the right, versus algorithms that can query vectors on both the left and the right. We also show separations depending on the underlying field the matrix-vector product occurs in. For graph problems, we show separations depending on the form of the matrix (bipartite adjacency versus signed edge-vertex incidence matrix) to represent the graph. Surprisingly, very few works discuss this fundamental model, and we believe a thorough investigation of problems in this model would be beneficial to a number of different application areas.

The Development of the Method of Radar Observation System Construction of the Airspace on the Basis of Genetic Algorithm

The methodological approaches to the use of genetic algorithm for the synthesis of the rational structure of the radar surveillance system are proposed in the paper. The structure of the radar surveillance system is presented in the form of an incidence matrix, which is used as a chromosome by the operators of the genetic algorithm. This matrix is used as a chromosome by the operators of the genetic algorithm. The elements of the incidence matrix that describe the relationships between the elements of the structure of the observation system are genes in the genetic algorithm. In each cycle of the genetic algorithm, a pair of chromosomes is paired, during which part of the genes are exchanged, which for the system under study means the appearance and disappearance of the corresponding connections between the elements. The calculation of the values of the efficiency of radar surveillance for each variant of the structure is proposed to be carried out using the ant colony optimization. The gain in the value of the conditional probability of correct detection with a fixed probability of false alarm is approximately 10% Keywords— genetic algorithm, artificial intelligence, optimization, route, radar surveillance system.

The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession

The traditional machine-part cell formation problem simultaneously clusters machines and parts in different production cells from a zero–one incidence matrix that describes the existing interactions between the elements. This manuscript explores a novel alternative for the well-known machine-part cell formation problem in which the incidence matrix is composed of non-binary values. The model is presented as multiple-ratio fractional programming with binary variables in quadratic terms. A simple reformulation is also implemented in the manuscript to express the model as a mixed-integer linear programming optimization problem. The performance of the proposed model is shown through two types of empirical experiments. In the first group of experiments, the model is tested with a set of randomized matrices, and its performance is compared to the one obtained with a standard greedy algorithm. These experiments showed that the proposed model achieves higher fitness values in all matrices considered than the greedy algorithm. In the second type of experiment, the optimization model is evaluated with a real-world problem belonging to Human Resource Management. The results obtained were in line with previous findings described in the literature about the case study.

UNION AND INTERSECTION OF CHAOTIC GRAPHS WITH DENSITY VARIATION

The aim of the present study is to discuss the union and intersection operations on chaotic graphs with density variation; the adjacency and incidence matrices representing the chaotic graphs induced from these operations will be introduced when physical characters of chaotic graphs have the same properties. There are several applications that have been utilized on chaotic graphs with density variation. The most practical applications of these kinds of operations on chaotic graphs with density variation are the internet signal speeds and the variation of green color for different parts of the plant. For example, in botany, in some cases, several plants suffer from a lack of chlorophyll in the damaged parts of the plant. In this case, the plant is represented by a chaotic graph, and the proportion of chlorophyll is represented by the density property, then the appropriate process is applied to increase the chlorophyll percentage in the appropriate place, so these operations help us to choose the suitable operator that satisfies our desires and requests. Keywords: adjacency matrix, incidence matrix, chaotic graph, density, union, intersection.

Evaluating Popular Statistical Properties of Incomplete Block Designs: A MATLAB Program Approach

Evaluating the statistical properties of a semi-Latin square, and in general, an incomplete block design, is vital in determining the usefulness of the design for experimentation. Improving the procedures for obtaining these statistical properties has been the subject of some research studies and software developments. Many available statistical software that evaluate incomplete block designs do so at the level of analysis of variance but not for the popular A-, D-, E-, and MV-efficiency properties of these designs to determine their adequacy for experimentation. This study presents a program written in the MATLAB environment using MATLAB codes and syntaxes which is capable of computing the A-, D-, E-, and MV-efficiency properties of any n×n/k semi-Latin square and any incomplete block design via their incidence matrices, where N is the number of rows and columns and k is the number of plots. The only input required for the program to compute the four efficiency criteria is the incidence matrix of the incomplete block design. The incidence matrix is the binary representation of an incomplete block design. The program automatically generates the efficiency values of the design once the incidence matrix has been provided, as shown in the examples.

Identifying cell types from single-cell data based on similarities and dissimilarities between cells

Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.