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June 25, 2026
HSE Researchers Make Aldehydes Perform Dual Function
Chemists from HSE University have discovered a way to carry out a reductive addition reaction without using an external reducing agent. Instead, the required 'resource' is supplied by the aldehyde itself, one of the reaction participants. This approach helps prevent unwanted side reactions, reduces toxicity, and simplifies the production and synthesis of organic molecules, including those used in the manufacture of medicines. The study has been published in Journal of Catalysis.
June 25, 2026
HSE Scientists Explain Why Findings in Autism Research Differ
Researchers from the Cognitive Health and Intelligence Centre at HSE University conducted the first-ever systematic review of studies on the specifics of emotion-from-motion perception in autism. The review showed that differences found between autistic and non-autistic individuals are largely associated with the experimental design and the types of tasks given to study participants. The review findings have been published in Research in Autism.
June 22, 2026
‘In Science, You Are Your Own Boss
Polina Nasledskova is interested in identifying gaps in linguistics and topics that have been overlooked by other researchers. In an interview for the  Young Scientists of HSE University project, she spoke about rare ordinal numerals in Nakh-Daghestanian languages, the benefits of knitting for concentration, and the beauty of the Patriarshy Bridge.

 

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Neural networks-based data hiding in digital images: overview

Neurocomputing. 2024. Vol. 581. Article 127499.
Dzhanashia K., Evsyutin O.

Nowadays, neural networks are actively used for data hiding; however, there is currently no systematic knowledge regarding their utilization in this field. This is a significant gap, considering that neural network-based data hiding has already formed a large and quite independent area of research. This review aims to provide such systematization. It also provides a general framework of neural network usage for data hiding, comparisons of quantitative and qualitative indicators of the effectiveness of existing data hiding methods, and discussion and conclusions containing the most promising directions and tools for research in the field of data hiding using neural networks, as well as the main achievements and the persisting challenges. This work targets researchers looking for the most suitable data hiding method for their application, as well as the developers of data hiding methods.

Research target: Computer Science
Language: English
DOI
Text on another site
Keywords: digital imagesteganographydata hidingwatermarkingneural network
Publication based on the results of:
Research and development of data transmission and processing methods for the organization of energy efficient and secure data transmission in the Internet of Things networks (2024)
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