TREATMENT EXPERIENCE OF CHRONIC LUNG ABSCESSES USING MINI-INVASIVE ELECTROSURGICAL TECHNIQUES.

Objective: The aim: To increase the efficiency of surgical treatment of patients with chronic lung abscesses by developing mini-invasive methods of surgical treatment using electrosurgical technologies.

Patients And Methods: Materials and methods: Conducted study of the results of surgical treatment of 78 patients with chronic lung abscesses operated from 2011 to 2021. Patients were divided into two groups: the main group (37 patients who were treated using developed technologies) and a comparison group (41 patients, treated using traditional tactics).

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.

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.

Cognitive biases and excessive use of social media: The facebook implicit associations test (FIAT).

Many theoretical accounts of addictive behaviors, including models of Internet use disorders, implicate cognitive biases in the formation and maintenance of excessive behaviors. Yet, little empirical evidence regarding the role of such biases, including implicit attitude, in the development and maintenance of excessive use of social media exists. We seek to bridge this gap in this study.

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.