We study the special case of a nonlinear stochastic consumption model taking the form of a 2-dimensional, non-invertible map with an additive stochastic component. Applying the concept of the stochastic sensitivity function and the related technique of confidence domains, we establish the conditions under which the system's complex consumption attractor is likely to become observable. It is shown that the level of noise intensities beyond which the complex consumption attractor is likely to be observed depends on the weight given to past consumption in an individual's preference adjustment.

A concept of optically triggered and electrically controlled ultra-fast neuromorphic computing processor based on an antiferromagnetic/heavy metal (AFM/HM) heterostructure is proposed. The AFM/HM-based artificial neurons are excited with short THz-range pulses, triggering precession in AFM. Bias electric current in the HM layer can be used to modify the resonance frequency of precession. The conversion of the precession into the electric current in the HM-layer occurs via the inverse spin Hall effect. A model of a neuromorphic processor is, thus, proposed, consisting of excitatory AFM-based artificial neurons—oscillators, and processing neurons—detectors. We show that the use of optical excitation can significantly increase the processing speed of neuromorphic computing at low power consumption. Examples of the implementation of the simplest logical operations (OR, AND) are demonstrated.

Goal: Because of the outbreak of coronavirus infection, healthcare systems are faced with the lack of medical professionals. We present a system for the differential diagnosis of coronavirus disease, based on deep learning techniques, which can be implemented in clinics. Methods: A recurrent network with a convolutional neural network as an encoder and an attention mechanism is used. A database of about 3000 records of coughing was collected. The data was collected through the Acoustery mobile application in hospitals in Russia, Belarus, and Kazakhstan from April 2020 to October 2020. Results: The model classification accuracy reaches 85%. Values of precision and recall metrics are 78.5% and 73%. Conclusions: We reached satisfactory results in solving the problem. The proposed model is already being tested by doctors to understand the ways of improvement. Other architectures should be considered that use a larger training sample and all available patient information.

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