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October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
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Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
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The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Semi-fragile image watermarking: A comprehensive survey, classification, and research directions

Computer Science Review. 2027. Vol. 63. No. A. Article 101066.
Melman A., Evsyutin O.

One of the effective means of protecting digital images is invisible watermarking. Different scenarios have different requirements for watermark resistance to post-processing. Robust watermarking schemes are resistant to a wide range of modifications. Fragile watermarking schemes are designed for integrity control and are destroyed by even minor modifications of images. Semi-fragile watermarking schemes combine features of robust and fragile ones to solve image authentication problems. The design of such schemes ensures that the authenticity of images is confirmed under "harmless" modifications such as compression, brightness changes, blurring, or rotation. However, if the image content is tampered, for example, by inserting or deleting an object, image authenticity verification reveals the forgery. To date, researchers have proposed many different approaches to ensuring semi-fragility of watermark embedding for digital images, including the use of traditional embedding methods and the use of neural networks. In this study, we propose a classification of semi-fragile image watermarking schemes and a review of current studies in this field. We analyze known schemes in terms of watermark type, tamper detection approach, and the ability to localize and recover the tampered regions. We also discuss the effectiveness of the considered schemes in terms of embedding imperceptibility, robustness, and fragility. Based on the review results, we highlight the challenges in semi-fragile image watermarking and formulate promising research directions that may be useful to researchers in the field of developing digital image authentication tools.

Research target: Computer Science
Language: English
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Keywords: deepfake detectionimage watermarkingsemi-fragile watermarkingimage authenticationtamper localizationtamper recovery
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