In this paper, we provide a new family of trivariate proper quasi-copulas. As an application, we show that W3 - the best-possible lower bound for the set of trivariate quasi-copulas (and copulas) - is the limit member of this family, showing how the mass of W3 is distributed on the plane x+y+z=2 of [0,1]3 in an easy manner, and providing the generalization of this result to n dimensions.
A new form of α-compactness is introduced in L-topological spaces by α-open L-sets and their inequality where L is a complete de Morgan algebra. It doesn’t rely on the structure of the basis lattice L. It can also be characterized by means of α-closed L-sets and their inequality. When L is a completely distributive de Morgan algebra, its many characterizations are presented and the relations between it and the other types of compactness are discussed. Countable α-compactness and the α-Lindelöf property are also researched.
A new annual species, Juncus maroccanus, of the section Tenageia, closely allied to Juncus foliosus Desf., is described from N Morocco. It differs from the latter in having smooth, glossy seeds, capsule shorter than perianth and shortly mucronate. The new species is known from a macrolocality in the Ksar-el-Kebir region, where it grows in non-saline sandy seepage sites. Another, much older specimen was collected in 1835 by W. Schimper in the Sinai Peninsula, Egypt. Syntype specimens of Juncus rhiphaenus Pau et Font Quer were examined and found to be conspecific with Juncus foliosus.
A close relationship between the class of totally positive matrices and anti-Monge matrices is used for suggesting a new direction for investigating totally positive matrices. Some questions are posed and a partial answer in the case of Vandermonde-like matrices is given., Miroslav Fiedler., and Obsahuje seznam literatury
Combinatorial optimization is a discipline of decision making in the case of diserete alternatives. The Genetic Neighborhood Search (GNS) is a hybrid method for these combinatorial optimization problems. The main feature of the approach is iterative use of local search on extended neighborhoods, where the better solution will be the center of a new extended neighborhood. When the center of the neighborhood would be t.he better solution the algorithm will stop. We propose using a genetic algorithm to exi)lore the extended neighborhoods. This GA is characterized by the method of evaluating the fitness of individuals and useing two new operators. Computational experience with the Symmetric TSP shows that this approach is robust with respect to the starting point and that high quality solutions are obtained in a reasonable time.
A method of reduction of dimensionality in discriminant analysis has been specially designed for this study of separation between two populations and it is only based on the heterocedasticity
of both groups. For this reason, it is of interest in the case of zero mean difference vector. Two examples of application, concerning the membership problem in open clusters from uvby and
Hβ photometry and the separation of globular clusters into Oosterhoff's groups are shown.
Galactlc bars are thlcker than Inner parts of gas dlsks. Therefore
gas flow Into theback side of bars, relatlve to the dlrectlon of dlsc
rotation, Thls Idea, based on the equlllbrium condltlon for galactlc rotating subsystems Is used to Interprete forbldden line emlsslon In bar regione vhere gas Inflow supersonic.
The suggested interpretation of the phenomenon of dlfferent velocity fields in the linea of dlfferent excltetion first discovered in the Seyfert galaxy Mkn 744 • NGC 3786 is now checked in the case of another Seyfert NGC 1068. .The appllcabillty Is controled from the Inferences concemlng the sense of rotation of spiral arms In the galaxies. In both cases these agree wlth the earlier results obtalned from Independent methods t the arms In NGC 1068 are trailling, while in NGC 3786 they are leadlng. One of manifestations of the shock back along the front slde of bars Is the occurence of stralght dust lanes along the front aide. Applications of the method
to other observational problems are discussed.
The multilayer perceptron model has been suggested as an alternative to conventional approaches, and can accurately forecast time series. Additionally, several novel artificial neural network models have been proposed as alternatives to the multilayer perceptron model, which have used (for example) the generalized- mean, geometric mean, and multiplicative neuron models. Although all of these artificial neural network models can produce successful forecasts, their aggregation functions mean that they are negatively affected by outliers. In this study, we propose a new multilayer, feed forward neural network model, which is a robust model that uses the trimmed mean neuron model. Its aggregation function does not depend on outliers. We trained this multilayer, feed forward neural network using modied particle swarm optimization. We applied the proposed method to three well-known time series, and our results suggest that it produces superior forecasts when compared with similar methods.
Neural networks have shown good results for detecting a certain pattern in a given image. In this paper, faster neural networks for pattern detection are presented. Such processors are designed based on cross correlation in the frequency domain between the input matrix and the input weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the searching process. The principle of divide and conquer strategy is applied through matrix decomposition. Each matrix is divided into submatrices small in size, and then each one is tested separately by using a single faster neural processor. Furthermore, faster pattern detection is obtained by using parallel processing techniques to test the resulting submatrices at the same time, employing the same number of faster neural networks. In contrast to faster neural networks, the speed-up ratio is increased with the size of the input matrix when using faster neural networks and matrix decomposition. Moreover, the problem of local submatrix normalization in the frequency domain is solved. The effect of matrix normalization on the speed-up ratio of pattern detection is discussed. Simulation results show that local submatrix normalization through weight normalization is faster than submatrix normalization in the spatial domain. The overall speed-up ratio of the detection process is increased as the normalization of weights is done offline.
A new model for propagation of long waves including the coastal area is introduced. This model considers only the motion of the surface of the sea under the condition of preservation of mass and the sea floor is inserted into the model as an obstacle to the motion. Thus we obtain a constrained hyperbolic free-boundary problem which is then solved numerically by a minimizing method called {\em the discrete Morse semi-flow}. The results of the computation in 1D show the adequacy of the proposed model.