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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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EEG evidence for reproducible neural states during Buddhist Highest Yoga Tantra meditation

Scientific Reports. 2026. Vol. 16. Article 23560.
Mikhaylets E. V., Razorenova A. М., Chernyshev V. L., Norbu N., Sangpo L., Zhironkina Y. S., Kaplan A. Y.

Meditation offers a naturalistic paradigm for studying introspection, yet the neural dynamics of advanced tantric practices remain largely unexplored. Buddhist Highest Yoga Tantra (BHYT) comprises a sequence of eight dissolution stages culminating in the “clear light” state. We recorded EEG during eyes-closed BHYT meditation performed in monasteries and hermitages (51 sessions from 36 male practitioners; age 31–69 years); analyses compared sessions within this single cohort, and some participants contributed multiple sessions. Using a state-detecting algorithm that combines EEG power spectrum and EEG synchronization features, we segmented each session into discrete EEG-defined states and clustered them. Six reproducible state types emerged, forming three symmetric pairs dominated by delta, alpha/theta, and beta/gamma activity, along with two background-like states. Representing each meditation as a 8-symbolic alphabet sequence enabled quantitative comparison across sessions and practitioners. Although all followed the same canonical BHYT protocol, sequences differed systematically and formed six meditation clusters. Greater expertise was associated with longer and more diverse states within a meditation, with increased presence of oscillatory alpha/theta and beta/gamma states, whereas lower-experience sessions were represented by shorter sequences dominated by background or delta activity. These findings suggest BHYT practice unfolds as structured neural state sequences and provide a quantitative bridge between canonical contemplative descriptions and measurable EEG dynamics.

Research target: Computer Science Mathematics Biology
Language: English
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Keywords: кластеризация временных рядовtime-series clusteringEEGЭЭГmeditationмедитация
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