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July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Heuristic for a Real-life Truck and Trailer Routing Problem

Procedia Computer Science. 2014. Vol. 31. P. 778–792.
Mikhail Batsyn, Alexander Ponomarenko

In this paper we suggest a multi-start greedy heuristic for a real-life truck and trailer routing problem. The considered problem is a site dependent heterogeneous fleet truck and trailer routing problem with soft and hard time windows and split deliveries. This problem arises in delivering goods from a warehouse to stores of a big retail company. There are about 400 stores and 100 vehicles for one warehouse. Our heuristic is based on sequential greedy insertion of a customer to a route with further improvement of the solution. The computational experiments are performed for real-life data. We also provide a mixed integer linear programming formulation for precise and clear description of the problem.

Priority areas: IT and mathematics
Language: English
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Keywords: real-life problemtruck and trailer routing problemmulti-start heuristicgreedy insertion
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