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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.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Hey Google, how valuable is that startup? Internet search queries and new ventures’ valuation—insights from B2B and B2C sectors

Financial Innovation. 2026. Vol. 12. Article 34.
Malyy M., Tekic Z., Podladchikova T.

Valuing startups is inherently challenging due to high uncertainty and information asymmetry, which undermines the effectiveness of conventional valuation methods and compels investors to rely on intuition or qualitative judgments, often leading to inconsistent or manipulated valuations. This study explores the potential of internet search query data, particularly Google Trends, as a third-party signal to increase the accuracy of startup valuations. Grounded in signaling theory, we analyze valuation data from U.S. startups in two industry segments—B2C food delivery and B2B money lending—paired with their Google Trends data. Regression analysis reveals quadratic models with strong explanatory and predictive power, demonstrating that Google Trends data can reliably estimate a startup’s market value by contextualizing its search query data within its competitive landscape and incorporating a few known valuation points. This indicates that anonymized and aggregated search query data from reputable search engines (e.g., Google, Yandex, and Baidu), when analyzed in relation to competitors’ search data, act as credible third-party signal of market interest and serve as a form of certification of a startup's unobservable quality. Our research contributes to the literature on third-party signaling, startup valuation, and the use of digital signals in business decision-making, with practical implications for more transparent and data-driven valuation processes.

Research target: Economics and Management
Language: English
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Text on another site
Keywords: венчурный капиталоценка стоимости компанииventure capitalстартапыGoogle TrendsGoogle Trendsstartupssignaling theoryNonfinancial informationНефинансовая информация
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