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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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A Versatile Simulator for Complex Cluster Workloads

P. 300–318.
Makogon A., Oleg Sukhoroslov

Resource allocation and scheduling algorithms play a crucial role in operation of modern computing clusters. The algorithms used and their settings can significantly impact cluster utilization, application performance, and user experience. This area of research requires the use of simulators to quickly test a hypothesis or conduct comparative testing of different algorithms. This paper presents ClusterSim, a flexible and fast simulator of computing cluster and workloads. In contrast to other existing simulators, it supports both detailed modeling of complex workloads and fine-grained resource allocation, which allows to apply it for various problem statements and scenarios. At the same time, the basic workload scenarios are also supported, in which high performance is achieved. The conducted experiments demonstrate the applicability of ClusterSim for running large-scale simulations, modeling different scheduling approaches, and studying complex problem statements.

Language: English
DOI
Text on another site
Keywords: scheduling Resource allocationSimulationCompute clusterWorkload modelingAsynchronous programming

In book

Supercomputing. 10th Russian Supercomputing Days, RuSCDays 2024, Moscow, Russia, September 23–24, 2024, Revised Selected Papers, Part I
* 1. , Springer, 2025.
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