Nonlinear suboptimal filtration of pseudorandom sequences

The critical importance of studies of alternative methods of representation and processing of signals constructed from pseudorandom sequences of maximal period is demonstrated. For the first time, it is proposed that a multidimensional nonlinear finite-difference representation of an arbitrary pseudorandom sequence be employed. A suboptimal algorithm for nonlinear discrete Kalman filtration of a pseudorandom sequence is obtained. A numerical example of filtration of a sequence formed on the basis of a given recurrence rule is considered and its efficiency is demonstrated.     

Nonlinear parametric identification based on the criterion of minimum probability of estimation error

The importance of studies of alternative (as compared to traditional) methods of nonlinear stochastic parametric identification is demonstrated. Synthesis of a procedure of identification of the parameter, “discrete observer,” with the use of the criterion, “minimum probability of estimation error,” is proposed for the first time. A model example that demonstrates the efficiency of the proposed approach is considered.     

Derivative based global sensitivity measures and their link with global sensitivity indices

A model function f(x₁,…,x_n) defined in the unit hypercube Hⁿ with Lebesque measure dx = dx₁…dx_n is considered. If the function is square integrable, global sensitivity indices provide adequate estimates for the influence of individual factors x_i or groups of such factors. Alternative estimators that require less computer time can also be used. If the function f is differentiable, functionals depending on ∂f/∂x_i have been suggested as estimators for the influence of x_i. The Morris importance measure modified by Campolongo, Cariboni and Saltelli μ  * is an approximation of the functional μi=_∫Hn|∂f/∂xi|dx${\mu }_i={\int }_{H^n}\left|\partial f\text{/}\partial x_i\right|dx$.     

Solution of the problem of identifying structures of discrete stochastic objects based on the minimum posterior error criterion of distribution densities

The importance of studying methods for solving the problem of identifying structures of discrete stochastic objects has been shown. The general structure of an identification algorithm based on the use of generalized probabilistic criteria has been proposed. The structural identification algorithm is synthesized based on the minimum posterior error criterion of distribution densities. A numerical example that makes it possible to assess the effectiveness of the proposed approach has been considered.     

Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows

Automatic synthesis of realistic gestures promises to transform the fields of animation, avatars and communicative agents. In off-line applications, novel tools can alter the role of an animator to that of a director, who provides only high-level input for the desired animation; a learned network then translates these instructions into an appropriate sequence of body poses. In interactive scenarios, systems for generating natural animations on the fly are key to achieving believable and relatable characters. In this paper we address some of the core issues towards these ends. By adapting a deep learning-based motion synthesis method called MoGlow, we propose a new generative model for generating state-of-the-art realistic speech-driven gesticulation. Owing to the probabilistic nature of the approach, our model can produce a battery of different, yet plausible, gestures given the same input speech signal. Just like humans, this gives a rich natural variation of motion. We additionally demonstrate the ability to exert directorial control over the output style, such as gesture level, speed, symmetry and spacial extent. Such control can be leveraged to convey a desired character personality or mood. We achieve all this without any manual annotation of the data. User studies evaluating upper-body gesticulation confirm that the generated motions are natural and well match the input speech. Our method scores above all prior systems and baselines on these measures, and comes close to the ratings of the original recorded motions. We furthermore find that we can accurately control gesticulation styles without unnecessarily compromising perceived naturalness. Finally, we also demonstrate an application of the same method to full-body gesticulation, including the synthesis of stepping motion and stance.     

Alkaline activation of coals and carbon-base materials

Data on the reactions and processes occurring under the conditions of the alkaline activation of carbon substances—the production of activated carbons by the thermolysis of carbon substances in the presence of alkali metal hydroxides MOH—are summarized. The following most important activation processes were recognized: (1) the interaction of functional groups with MOH and the formation of intermediate structures with the C-O-M group; (2) their conversion into metal-containing compounds (primarily, M₂CO₃ and M₂O) in reactions with carbon, especially, with terminal C atoms on the periphery of graphenes; and (3) the reduction of M₂CO₃ and M₂O to the metal M, which is intercalated into the interlayer spaces of crystallites. The mechanism of alkaline activation was studied in most detail for KOH as an activating agent. The thermally initiated reduction of potassium oxide with carbon and the intercalation of potassium metal are the two most important processes for the development of the microporosity of activated carbon.     

Calculating first-order sensitivity measures: A benchmark of some recent methodologies

This work compares three different global sensitivity analysis techniques, namely the state-dependent parameter (SDP) modelling, the random balance designs, and the improved formulas of the Sobol’ sensitivity indices. These techniques are not yet commonly known in the literature. Strengths and weaknesses of each technique in terms of efficiency and computational cost are highlighted, thus enabling the user to choose the more suitable method depending on the computational model analysed. Two test functions proposed in the literature are considered. Computational costs and convergence rates for each function are compared and discussed.     

A criterion of minimum of estimation error probability in a problem of nonlinear parametric identification

Solution of stochastic nonlinear identification problem is proposed. An algorithm of discrete observer parameter identification, based on utilization of criterion of minimal estimation error probability, is synthesized. To illustrate proposed approach efficiency we consider a model example.     

Stress-strain state in the hydrostatic pressing of tubular parts from powder materials and method of calculating mold assembly elements

Conclusions An analysis is made of the mechanism of deformation of sizing shells in mold assemblies during the hydrostatic pressing of tubular parts from powder materials by the radial expansion technique. A plot has been obtained showing the distribution of pressure across the thickness of a part being pressed. A method is proposed for calculating the elements of mold assemblies for the hydrostatic pressing of tubular parts from powder materials by the radial expansion technique.Translated from Poroshkovaya Metallurgiya, No. 12(228), pp. 17–21, December, 1981.