OPERATOR’S TRUST IN EQUIPMENT IN THE SPACE OF SUBJECTIVE PROFESSIONAL WELL-BEING

Background. The relevance of the study is determined by the search for the determinants of the subjective professional well-being of operators. Possibilities of operators’ trust in equipment for achieving subjective professional well-being (SPWB) are considered. Objective. The analysis of the features of manifestation of operators’ trust in equipment as a psychological resource of subjective professional well-being was carried out. Design. The author’s questionnaire “Trust of the specialist to equipment (TSE)” was used to diagnose the level of trust of operators in equipment. Questionnaires: “Social and professional demand for personality”, “Methods for assessing professional well-being”, “Dominant state”, “Passionate about work” were used to diagnose SPWB. Empirical data were translated into z-scores. Z-scores were subjected to multidimensional scaling (non-metric scaling, distance function — Euclidean distance). The results were displayed in the form of two-dimensional graphic models of the psychological space of SPWB. In the SPWB space, assessments of indicators of operators’ trust in equipment and subjective professional well-being were reflected. Research sample. The study involved 76 NPP operators (specialists), 100 locomotive driver — a total of 176 people. Work experience — from 1 to 35 years. Results. The level of trust in the equipment among specialists is higher than among the locomotive drivers — 75.8 points and 62.4 points, respectively (p < 0,001). Experts’ assessments of indicators of trust in the equipment are located on the border of the SPWB space, far from the assessments of SPWB indicators. For locomotive drivers, assessments of indicators of trust in the equipment are located within the SPWB space next to 4 indicators of the SPWB. They reflected the desire for professional growth, satisfaction with the level of competence and professional achievements, the state of passion for work. Conclusions.The trust of specialists in the equipment becomes a psychological resource of subjective professional well-being in critical working situations. The trust of locomotive drivers in the equipment is a constant psychological resource for maintaining subjective professional well-being.

Variational Metric Scaling for Metric-Based Meta-Learning

Metric-based meta-learning has attracted a lot of attention due to its effectiveness and efficiency in few-shot learning. Recent studies show that metric scaling plays a crucial role in the performance of metric-based meta-learning algorithms. However, there still lacks a principled method for learning the metric scaling parameter automatically. In this paper, we recast metric-based meta-learning from a Bayesian perspective and develop a variational metric scaling framework for learning a proper metric scaling parameter. Firstly, we propose a stochastic variational method to learn a single global scaling parameter. To better fit the embedding space to a given data distribution, we extend our method to learn a dimensional scaling vector to transform the embedding space. Furthermore, to learn task-specific embeddings, we generate task-dependent dimensional scaling vectors with amortized variational inference. Our method is end-to-end without any pre-training and can be used as a simple plug-and-play module for existing metric-based meta-algorithms. Experiments on miniImageNet show that our methods can be used to consistently improve the performance of existing metric-based meta-algorithms including prototypical networks and TADAM.

Diagnosis using clinical/pathological and molecular information

In diagnosis and classification diseases multiple outcomes, both molecular and clinical/pathological are routinely gathered on patients. In recent years, many approaches have been suggested for integrating gene expression (continuous data) with clinical/pathological data (usually categorical and ordinal data). This new area of research integrates both clinical and genomic data in order to improve our knowledge about diseases, and to capture the information which is lost in independent clinical or genomic studies. The related metric scaling distance is a not well-known, but very valuable distance to integrate clinical/pathological and molecular information. In this article, we present the use of the related metric scaling distance in biomedical research. We describe how this distance works, and we also explain why it may sometimes be preferred. We discuss the choice of the related metric scaling distance and compare it with other proximity measures to include both clinical and genetic information. Furthermore, we comment the choice of the related metric scaling distance when classical clustering or discriminant analysis based on distances are performed and compare the results with more complex cluster or discriminant procedures specially constructed for integrating clinical and molecular information. The use of the related metric scaling distance is illustrated on simulated experimental and four real data sets, a heart disease, and three cancer studies. The results present the flexibility and availability of this distance which gives competitive results.