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September 4, 2026
‘Hedgehog Versus ‘Relatives: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
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.’

 

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Towards a Formal Modelling of Order-driven Trading Systems using Petri Nets: A Multi-Agent Approach

P. 92–103.
Carrasquel Gamez J. C., Lomazova I. A., Itkin I.

Electronic trading systems provide the computational support for stock exchanges. Liquid markets use order-driven systems, i.e., where client requests, for trading financial instruments, are served through individual orders. This paper presents Petri net models assembling some crucial processes executed within order-driven systems such as orders submission, application of precedence rules, and the order matching mechanism. Such processes were modelled as types of agents running in a multi-agent system (MAS) using nested Petri nets (NP-nets) - a convenient formalism for modelling MAS. With NP-nets, we focus on the control-flow perspective (causal dependence between activities executed by agents) and in the synchronization between agents. Conversely, we have used coloured Petri nets to extend the model including orders as objects with attributes. Thus, this work with Petri nets represents an experimental & initial research phase to validate trading systems using related methods such as process mining, simulations and model checking.

Language: English
Text on another site
Keywords: nested Petri netsPetri netsmulti-agent systemsprocess miningcolored Petri netstrading systems
Publication based on the results of:
Synthesis, recovery, conformance checking, and other methods for the analysis of process models and distributed information systems (2019)

In book

Proceedings of the MACSPro Workshop 2019
Vol. 2478: CEUR Workshop Proceedings. , CEUR-WS.org, 2019.
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