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  • ИТ в нефтегазе: разнообразие интересов
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News
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
June 3, 2026
Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.

 

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ИТ в нефтегазе: разнообразие интересов

Директор информационной службы. 2012. № 4. С. 20–24.
Polyakova M. V.
Research target: Computer Science Earth Sciences Biology
Priority areas: business informatics
Language: Russian
Text on another site
Keywords: нефтегазовый секторинформационные технологииавтоматизациясистемы управления предприятиемcовременное состояние и перспективы развитияинформационные службыместорождения нефти, газа и конденсатанефтепромысловая геологияресурсы нефти, газа и конденсата
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В статье про платформы для разработки открытого ПО в Китае мы рассказали про GitCode – молодой проект, позиционируемый как площадка для разработчиков со всего мира. Сейчас на GitCode размещаются проекты, созданные в КНР, но некоторые из них уже известны и на международной арене. Помочь открытым проектам в становлении, развитии и расширению аудитории призван фонд OpenAtom ...
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The recognition-by-components method
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The paper describes a applied artificial intelligence task of recognition-by-components method of real objects based on the recognition of a limited set of primitives or components. The recognition-by-components makes it possible to determine the components, that compose an object, and increase the number of recognizable objects without degrading the recognition quality. Training is performed on ...
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ИССЛЕДОВАНИЕ АССОЦИАЦИИ ГЕНЕТИЧЕСКИХ ВАРИАНТОВ С РАЗВИТИЕМ МУЗЫКАЛЬНЫХ СПОСОБНОСТЕЙ ЧЕЛОВЕКА
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The development of musical abilities, including absolute pitch, musical memory, rhythm sense, and musicality, at a high degree is determined by a hereditary component (up to 68 %). The studies implementing a genome-wide linkage and association approach to musical aptitude have revealed more than 100 genetic loci. This spectrum is comprised of the genes encoding ...
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Brain-Computer Interfaces for Gait Rehabilitation After Stroke A Scoping Review
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Brain-computer interfaces (BCIs) represent a promising technology for restoring lower limb motor functions and gait after stroke. The application of BCIs in this field is supported by a limited number of studies. The objective of the review was to systematically and critically evaluate the current evidence on the use of BCIs for lower limb function ...
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ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ И ТЕХНИЧЕСКИЕ СРЕДСТВА УПРАВЛЕНИЯ (ICCT-2024)
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Comparative Study of Training Methods and Architectures of Echo State Networks
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AI errors pose a significant challenge, hindering real-world applications. This work introduces a novel approach to cope with AI errors using weakly supervised error correctors that guarantee a specific level of error reduction. Our correctors have low computational cost and can be used to decide whether to abstain from making an unsafe classification. We provide ...
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High-dimensional tabular data are common in biomedical and clinical research, yet conventional machine learning methods often struggle in such settings due to data scarcity, feature redundancy, and limited generalization. In this study, we systematically evaluate Synolitic Graph Neural Networks (SGNNs), a framework that transforms high-dimensional samples into sample-specific graphs by training ensembles of low-dimensional pairwise ...
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