Concept Learning from Triadic Data

AbstractWe propose extensions of the classical JSM-method and the Näıve Bayesian classifier for the case of triadic relational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classification precision and recall we also evaluated the time performance of the proposed methods

Modeling of Financial and Economic Results of the System of Scientific and Scientific-Technical Activity of Higher Education Institution

The article is aimed at developing theoretical and methodological foundations for planning activities of the system of scientific and scientific-technical activity of higher education institution and to assess the influence of various factors on its financial and economic effectiveness. The models of dependence of financial and economic results of scientific and scientific-technical activity on the time are provided, the factors influencing their formation are determined and researched. The multi-factor regression models are built, describing dependence of the effective attributes – volumes of financial receipts in the general and the special funds of university budget due to the implementation of scientific research and development – from factor variables (number of published articles in the peer-reviewed scientific editions, as well as publications, indexed by scientometrical databases, number of patents). The developed models can be considered as an instrument for management of system of the scientific and scientific-technical activity of university on attraction of financing to the general fund of university from performance of scientific-research works (fundamental and applied research, scientific and technical developments, financed from the State budget of the Ministry of Education and Science of Ukraine)

Reinforcement Learning with Algorithms from Probabilistic Structure Estimation

Reinforcement learning (RL) algorithms aim to learn optimal decisions in unknown environments through experience of taking actions and observing the rewards gained. In some cases, the environment is not influenced by the actions of the RL agent, in which case the problem can be modeled as a contextual multi-armed bandit and lightweight \emph{myopic} algorithms can be employed. On the other hand, when the RL agent's actions affect the environment, the problem must be modeled as a Markov decision process and more complex RL algorithms are required which take the future effects of actions into account. Moreover, in many modern RL settings, it is unknown from the outset whether or not the agent's actions will impact the environment and it is often not possible to determine which RL algorithm is most fitting. In this work, we propose to avoid this dilemma entirely and incorporate a choice mechanism into our RL framework. Rather than assuming a specific problem structure, we use a probabilistic structure estimation procedure based on a likelihood-ratio (LR) test to make a more informed selection of learning algorithm. We derive a sufficient condition under which myopic policies are optimal, present an LR test for this condition, and derive a bound on the regret of our framework. We provide examples of real-world scenarios where our framework is needed and provide extensive simulations to validate our approach

The emergence of new psychoactive substance (NPS) benzodiazepines: a review

The market for new psychoactive substances has increased markedly in recent years and there is now a steady stream of compounds appearing every year. Benzodiazepines consist of only a fraction of the total number of these compounds but their use and misuse has rapidly increased. Some of these benzodiazepines have only been patented, some of them have not been previously synthesised and the majority have never undergone clinical trials or tests. Despite their structural and chemical similarity, large differences exist between the benzodiazepines in their pharmacokinetic parameters and metabolic pathways and so they are not easily comparable. As benzodiazepines have been clinically used since the 1960s many analytical methods exist to quantify them in a variety of biological matrices and it is expected that these methods would also be suitable for the detection of benzodiazepines that are new psychoactive substances. Illicitly obtained benzodiazepines have been found to contain a wide range of compounds such as opiates which presents a problem since the use of them in conjunction with each other can lead to respiratory depression and death. The aim of this review is to collate the available information on these benzodiazepines and to provide a starting point for the further investigation of their pharmacokinetics which is clearly required

Теорія та практика менеджменту безпеки

У збірнику подано тези доповідей та виступів учасників Міжнародної науково-практичної конференції, присвяченої питанням теорії менеджменту безпеки, безпеки особистості, прикладним аспектам забезпечення соціальної, екологічної, економічної безпеки підприємств, питанням механізму забезпечення соціоекологоекономічної безпеки регіону, проблемам забезпечення національної безпеки

Disposal of pine wood waste by pelleting with sulphate soap binder

Promising method for disposal the pine wood waste through extrusion pelleting using sulphate soap as a natural binder is considered in the article. Prior to pelleting, the wood waste requires drying to a water content of no more than 10%. Analysis of pine wood waste drying in filtration mode yielded optimal parameters: A 20 mm layer thickness, temperature of 135 °C, and drying time of 3,900 s. The optimal content of sulphate soap binder was determined to be about 20%, resulting in reduced coke residue, increased volatile components, higher calorific value, and enhanced static strength. This binder facilitates formation of pellets at lower pressures, increases calorific value, and acts as a lubricant, reducing friction and associated energy costs.

Automated Testing Algorithm for the Improvement of 1T1R ReRAM Endurance

One of the most attractive types of novel nonvolatile memory concepts is resistive random access memory (ReRAM) based on a reversible (“soft”) dielectric breakdown effect. The interest is caused by combining simple architecture with promising performance: excellent scalability, nanosecond speed, long data retention, and low power consumption. However, the commercialization of this type of memory is retarded mainly due to serious drawbacks: high cell-to-cell variability of switching parameters and limited endurance related to the ionic origin of resistive switching effect coupled with the problem of voltage overshooting. In particular, these issues complicate the examination of test samples because during the lifespan of memory cells, and the permanent reselection of test switching parameters is required. This challenge can be overcome by developing a measurement technique that combines careful testing of every single structure and the ability to test a large number of test structures. As such a technique, we propose an automated testing algorithm that automatically avoids voltage overshooting and provides an effective way of characterization of cell-to-cell and cycle-to-cycle variability. It includes all stages of ReRAM cell operation, specifically electroforming, dc switching, and endurance testing in the pulsed mode. The developed technique allows on-the-fly restoring of the memory window in case of its degradation, making it possible to better understand the potential of the material stacks of the memory cell