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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Queue Waiting Time Estimation Using Person Re-identification by Upper Body

P. 464–474.
Konushin A., Mamedov T., Kuplyakov D.

In this paper, we propose a new approach to estimating waiting time in queue based on object tracking and person re-identification by upper body. The task we are considering is practically important in video analysis, because this data can be used for predictive analytics and improvement of customer services. The main idea of the proposed method is to use upper body detections instead of full body detections. This decision is due to the following fact: in queues, the upper bodies are more visible. Using re-identification allows us to perform video analytics on sparse frames and thereby increase the computational efficiency of the estimation algorithm. Also in this work, we introduce a novel upper body regression by head, upper body random size augmentation to improve re-identification performance in real-world scenarios and a method for calculating metrics for queue waiting time estimation algorithms. Our experimental evaluation showed that the proposed algorithm has a high accuracy of queue waiting time estimation.

Language: English
DOI
Keywords: computer vision

In book

Proceedings of the 31st International Conference on Computer Graphics and Vision (GraphiCon 2021). Nizhny Novgorod, Russia, September 27-30, 2021
Vol. 3027. , CEUR Workshop Proceedings, 2021.
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