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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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О решении детерминированной и стохастической задачи домашнего хозяйства с конечным горизонтом планирования

Экономический журнал Высшей школы экономики. 2025. Т. 29. № 1. С. 42–71.
Pilnik N.

The article uses the example of an optimization problem of a household that makes a de­cision on the volumes of consumption and investment to show what difficulties arise in deterministic and stochastic formulations on a finite time interval. In order to make the problem solvableon a finite time interval, a special terminal condition on the agent's equity capital is added, gene­ralizing the standard versions of such conditions.

The article considers two settings. The first setting is a deterministic case, assuming that the household knows the trajectories of all exogenous variables over the entire time interval under consideration. An analytical solution to this problem is found and it is shown that by choosing the parameter of the terminal constraint in the problem on a finite time interval, it is always possible to obtain a consumption trajectory from the solution of a similar problem set for an infinite planning horizon. If the coefficient of the terminal condition is chosen so that the optimal consumption trajectory continues the previous value, then with a certain combination of initial conditions, the household's problem can either be solvable only up to a certain planning horizon, or be completely unsolvable.

The second statement is a stochastic case, when the household knows only the distribution law of exogenous variables. In this case, it is not possible to provide a complete analytical solution, but a sequential algorithm is proposed that allows one to obtain a step-by-step description of the calculation of such a solution. The study of the properties of the constructed model al­lows one to show how different the work with stochastic optimization problems for the analysis of deviations from a certain selected trajectory of states (balanced growth) in response to the implementation of other states (shocks) is from the problem of analyzing specific realized trajectories of the agent's variables.

Research target: Economics and Management Mathematics
Language: Russian
DOI
Text on another site
Keywords: finite planning horizonbalanced growthhousehold optimization problemoptimality conditions in Lagrange formterminal conditionsоптимизационная задача домохозяйстватерминальные условияконечный горизонт планированияусловия оптимальности в форме Лагранжасбалансированный рост
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