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News
September 4, 2026
‘Hedgehog Versus ‘Relatives: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’

 

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Watermarking for social networks images with improved robustness through polar codes

IEEE Access. 2024. Vol. 12. P. 118154–118168.
Evsyutin O., Ivanov F., Dzhanashia K.

Protecting ownership of digital content is challenging in today’s online world, especially when sharing content through social networks and instant messengers. One possible solution is the use of watermarking; however, if the watermarking method is not robust enough, the watermark can get damaged or erased during transmission. This study introduces a template-based watermarking method with neural network-based extraction, strengthened by error-correcting polar codes, that is designed to work well on major social networks like Facebook, Facebook Messenger, VK, Telegram, Snapchat, Pinterest, and WhatsApp. The method can embed imperceptible (PSNR=39.66) watermarks ranging from 650 to 1600 bits for high-definition images (1920-by-1080 pixels) with small probability (0.01) of erroneous extraction after being transferred through social networks. The key feature of the method is its ability to work with real communication channels, as shown during its testing.

Research target: Computer Science
Language: English
DOI
Text on another site
Keywords: social networkdigital image social mediawatermarkingpolar codesneural network
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