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${\mu }_i={\int }_{H^n}\left|\partial f\text{/}\partial x_i\right|dx$.     

Illness and injury in family day care: a seasonal survey

Many mothers with children under age 6 are employed and it is not usually feasible for a parent to stay home with a child who is mildly ill. Such ill children likely remain in child day care programs. The extent to which this occurs and the management of these children in family day care was studied. Over the course of a year, 1 to 3 visits were made to 714 family day care homes in order to survey providers. With enrollments ranging between 0 and 18 children per day care home at the time of each visit, information on 3,630 "child enrollment days" was collected. The median age of children in care was 2 years. An average of 16% of all children were ill on any one day (with seasonal variation); 1% were injured. Of ill children, 82% attended day care that day, 49% had contacted a physician about that illness, and 28% were administered a medication at the day care setting. Twenty-one percent of children receiving medication in day care had no contact with a physician for that illness. These data show that mild childhood illnesses are routinely managed by child day care providers. Physicians who traditionally limit their illness-management education to parents need to recognize the health education and consultation needs of day care providers.     

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.