Memristor-based spiking neural network with online reinforcement learning.

Neural networks implemented in memristor-based hardware can provide fast and efficient in-memory computation, but traditional learning methods such as error back-propagation are hardly feasible in it. Spiking neural networks (SNNs) are highly promising in this regard, as their weights can be changed locally in a self-organized manner without the demand for high-precision changes calculated with the use of information almost from the entire network. This problem is rather relevant for solving control tasks with neural-network reinforcement learning methods, as those are highly sensitive to any source of stochasticity in a model initialization, training, or decision-making procedure.

On the accuracy of Covid-19 forecasting methods in Russia for two years.

The effectiveness of predicting the dynamics of the coronavirus pandemic for Russia as a whole and for Moscow is studied for a two-year period beginning March 2020. The comparison includes well-proven population models and statistic methods along with a new data-driven model based on the LSTM neural network. The latter model is trained on a set of Russian regions simultaneously, and predicts the total number of cases on the 14-day forecast horizon.

Baseline Accuracies of Forecasting COVID-19 Cases in Russian Regions on a Year in Retrospect Using Basic Statistical and Machine Learning Methods.

The large amount of data accumulated so far on the dynamics of the COVID-19 outbreak has allowed assessing the accuracy of forecasting methods in retrospect. This work compares several basic time series analysis methods, including machine learning methods, for forecasting the number of confirmed cases for some days ahead. Year-long data for all regions of Russia has been used from the Yandex DataLens platform.

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights.

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above.

[Factors significant for cerebral circulacion in patients with supratentorial brain tumors].

Using the Doppler ultrasonography method the condition of brain blood circulation of 90 patients with supratentorial brain tumors (gliomas--43, meningiomas--34, metastasis--9) during pre-surgical period was studied. The factors changing brain blood circulation at patients with with supratentorial brain tumors were brain displacement, increase of intracranial pressure, histologic structure and the first symptoms duration of illness. Localization (for an exception of an occipital lobe) and the size of a tumor directly didn't render influence on blood circulation parameters.

Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters.

The aim of this study was to develop an artificial neural networks-based (ANNs) diagnostic model for coronary heart disease (CHD) using a complex of traditional and genetic factors of this disease. The original database for ANNs included clinical, laboratory, functional, coronary angiographic, and genetic [single nucleotide polymorphisms (SNPs)] characteristics of 487 patients (327 with CHD caused by coronary atherosclerosis, 160 without CHD). By changing the types of ANN and the number of input factors applied, we created models that demonstrated 64-94% accuracy.

[Qualitative assessment of a noncarcinogenic risk to human health].

The existing methodic approaches to analyzing a noncarcinogenic risk fail to fully solve the tasks set within the basic lines of the activities of the Russian Agency for Consumer Surveillance since there are limited capacities of the quantitative assessment of a noncarcinogenic risk to human health. An algorithm is proposed for basing the indicators assessing a noncarcinogenic risk to human health, which assumes to determine exposure or an exposure marker for a cohort to be examined, to define a response to human health exposure, to construct mathematical "exposure (an exposure marker)-response" models, to determine the ineffective levels exposure for each type of a response, to make the piecewise-linear approximation of a model, and to calculate a slope factor for each linearized interval of an exposure-response model. Application of the proposed methodic approaches makes it possible, provided that the estimation of the cost of risk units, to assess the economic loss risk associated with the pollution of environmental objects, including a preventable risk, and to calculate the indicators of the effectiveness and efficiency of the activities of the bodies and organizations of the Russian Agency for Consumer Surveillance in reducing the risk to the population's health.