The article discusses the display of computer generated holograms of 3D objects using a binary amplitude spatial light modulator. More precisely, it deals with binarization of a diffractive structure using dithering noise. It concludes that the dithering method is suitable for simple diffractive structures such as diffractive gratings, but fails to provide a good binarization of computer generated holograms of 3D objects., Článek se zabývá zobrazením počítačem generovaného hologramu 3D objektu na binárním amplitudovém prostorovém modulátoru světla, respektive binarizací difraktivních struktur. Rozvíjí myšlenku binarizace difraktivní struktury pomocí techniky ditheringu. Dospívá k závěru, že technika je vhodná pro jednoduché struktury typu difrakční mřížky, ale není vhodná pro počítačem generované hologramy 3D objektu., and Pokračování článku v příštím čísle
Consideration of mass transfer and mass loss processes allows the explanation of the existence of well determined groups of binaries, such as Algols, cataclysmic variables, massive and low masa X ray binaries. The characteristics of these groups are briefly outlined, and evolutionary scenarios are discussed. The importance of detailed observations for advances in evolutionary computations are stressed. It isargued that, although the linka between the mentionned groups of binaries and their progenitors is rather well known, the situation is completely different if the detailed evolutionary history for a given system is required.
The unsupervised learning of feature extraction in high-dimesional patterns is a central problem for the neural network approach. Feature extraction is a procedure which maps original patterns into the feature (or factor) space of reduced dimension. In this paper we demonstrate that Hebbian learning in Hopfield-like neural network is a natural procedure for unsupervised learning of feature extraction. Due to this learning, factors become the attractors of network dynamics, hence they can be revealed by the random search. The neurodynamics is analysed by Single-Step approximation which is known [8] to be rather accurate for sparsely encoded Hopfield-network. Thus, the analysis is restricted by the case of sparsely encoded factors. The accuracy of Single-Step approximation is confirmed by Computer simulations.
We deal with a sequencing problem that arises when there are multiple repair actions available to fix a broken man-made system and the true cause of the system failure is uncertain. The system is formally described by a probabilistic model, and it is to be repaired by a sequence of troubleshooting actions designed to identify the cause of the malfunction and fix the system. The task is to find a course of repair with minimal expected cost. We propose a binary integer programming formulation for the problem. This can be used to solve the problem directly or to compute lower bounds of the minimal expected cost using linear programming relaxation. We also present three greedy algorithms for computing initial feasible solutions.
Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the results of some well known Machine Learning methods in the resolution of discrete classification problems. A binary version of the PSO algorithm is used to obtain a set of logic rules that map binary masks (that represent the attribute values), to the available classes. This algorithm has been tested both in a single pass mode and in an iterated mode on a well-known set of problems, called the MONKS set, to compare the PSO results against the results reported for that domain by the application of some common Machine Learning algorithms.
We introduce the function Z(x;ξ,ν):=∫x−∞φ(t−ξ)⋅Φ(νt)dt, where φ and Φ are the pdf and cdf of N(0,1), respectively. We derive two recurrence formulas for the effective computation of its values. We show that with an algorithm for this function, we can efficiently compute the second-order terms of Bonferroni-type inequalities yielding the upper and lower bounds for the distribution of a max-type binary segmentation statistic in the case of small samples (where asymptotic results do not work), and in general for max-type random variables of a certain type. We show three applications of the method -- (a) calculation of critical values of the segmentation statistic, (b) evaluation of its efficiency and (c) evaluation of an estimator of a point of change in the mean of time series.
The area of biomedicine is one of the fastes developing areas of science and technology. The perception of its possible and wxpected positive or negative impacts results in the growing number of bioethical discussions in scientific community, politics and public. Their intensity, focus and used methods differ from country to country. he authors of the prologue have tried to map the state of the art and expected development of bioethical discussion in the coutnries of Middle and Eastern Europe. In the beginning, they addressed the bioethical experts with short questionnaire from 7 "new" European coutries (Croatia, Czech Republic, Hungary, Poland, Romania, Slovakia and Slovenia) and two "old" European coutries (Germany and Austria). In the end, seven experts have responde their question (Czech Republic, Poland, Romania, Slovakia, Austria and Germany) and expressed their expectations and difficulties of the development of bioethical discussions and institutionalisations in their countries. The authors summarize in the prologue the most interesting results. and Gerhard Banse, Monika Bartíková.