12th International Symposium on Computer Science in Sport. Book of Abstracts

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12th International Symposium on Computer Science in Sport. Book of Abstracts

M. : Государственное казенное учреждение города Москвы "Центр спортивных инновационных технологий и подготовки сборных команд" Департамента физической культуры и спорта города Москвы, 2019.

Editor-in-chief: A. Danilov, M. Lames, Y. Timme, Y. Vassilevski

The 12th International Symposium of Computer Science in Sports (IACSS 2019), took place July 8-10, 2019 at Marchuk Institute of Numerical Mathematics of the Russian Academy of Science and the Moscow Center of Advanced Sports Technologies (MCAST), both situated in Moscow, Russia. The symposium continued a tradition of conferences starting in 1997 at Cologne, Germany, which were held biennially and travelled through many countries and continents since then. Though the topics of the presentations have changed, the aims of the symposium are still the same. The symposium engages in building links between computer science and sports science, and showcases a wide variety of applications of computer science techniques to a wide number of problems in sports and exercise sciences. Moreover, it provides a platform for researchers in both computer science and sports science for mutual understanding, discussing the respective ideas, and promoting cross-disciplinary research. This year the symposium addressed the following topics: Computer Science Sports and Exercise Science - Modeling and Simulation - Biomechanics and Neuromuscular Control - Sports Data Acquisition Systems - Exercise Physiology and Sports Medicine - Image and Video Processing - Performance Development and Analysis - Sports Data Analysis - Training, Coaching and Feedback - Machine Learning and Data Mining - Modelling of Adaptation, Fatigue, and Performance - Visualization and Visual Analytics - Optimization of Strategies for Best Performance - Presentation, Communication - Movement, Motor Control and Learning - Decision Support - Sports Management - Robotics - Virtual Reality - Digital Games We received 118 abstract submissions and all of them underwent reviews by the Program Committee...

Fantasy football meets machine learning: the dynamic game case and a note on strategy

Stolyarov A., Vasiliev G., , in: 12th International Symposium on Computer Science in Sport. Book of Abstracts.: M.: Государственное казенное учреждение города Москвы "Центр спортивных инновационных технологий и подготовки сборных команд" Департамента физической культуры и спорта города Москвы, 2019. P. 128–129.

Added: January 14, 2020

Research target: Medical and Health Sciences Computer Science Mathematics

Priority areas: IT and mathematics

Language: English

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12th International Symposium on Computer Science in Sport. Book of Abstracts

The recognition-by-components method

Mylnikov L., Slivnitsin P., Engineering Applications of Artificial Intelligence 2026 Vol. 179 Article 115185

The paper describes a applied artificial intelligence task of recognition-by-components method of real objects based on the recognition of a limited set of primitives or components. The recognition-by-components makes it possible to determine the components, that compose an object, and increase the number of recognizable objects without degrading the recognition quality. Training is performed on ...

Added: May 29, 2026

Electrical networks and data analysis in phylogenetics

Gorbounov Vassily, Kazakov A., Data Analytics and Topology 2025 Vol. 1 No. 1 P. 33–45

A classic problem in data analysis is studying the systems of subsets defined by either a similarity or a dissimilarity function on X which is either observed directly or derived from a data set. For an electrical network there are two functions on the set of the nodes defined by the resistance matrix and the response ...

Added: May 28, 2026

Brain-Computer Interfaces for Gait Rehabilitation After Stroke A Scoping Review

Mokienko O., Zisman M. A., Bobrov P. et al., American Journal of Physical Medicine and Rehabilitation 2026 Vol. 105 No. 6 P. 555–563

Brain-computer interfaces (BCIs) represent a promising technology for restoring lower limb motor functions and gait after stroke. The application of BCIs in this field is supported by a limited number of studies. The objective of the review was to systematically and critically evaluate the current evidence on the use of BCIs for lower limb function ...

Added: May 28, 2026

ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ И ТЕХНИЧЕСКИЕ СРЕДСТВА УПРАВЛЕНИЯ (ICCT-2024)

М.: Институт проблем управления им. В.А. Трапезникова РАН, 2024.

В сборник вошли материалы VIII Международной научной конференции «Информационные технологии и технические средства управления» (ICCT-2024). На конференции были рассмотрены вопросы, касающиеся перспектив развития научного приборостроения в телекоммуникационных и управляющих системах, биомедицинской информатики, аппаратного и программного обеспечения информационнокоммуникационных систем, надежности, диагностики и неразрушающего контроля, систем управления и автоматизации, цифровых экосистем, управления производством и логистикой, методов математического ...

Added: May 27, 2026

Non-linear in-band interference cancellation on base of conjugate gradients method

Degtyarev A., Bakhurin S., Yudin N., DSPA 2026 P. 1–6

This paper investigates one possible solution to the problem of self-interference cancellation (SIC) arising in the design of in-band full-duplex (IBFD) communication systems. Self-interference cancellation is performed in the digital domain using multilayer nonlinear models adapted via gradient-based optimization. The presence of local minima and saddle points during the adaptation of multilayer models limits the ...

Added: May 26, 2026

28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy – Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025)

IOS Press, 2025.

Added: May 26, 2026

New Numerical Invariants of an Unfolding of a Polycycle “Tears of the Heart”

Ilyashenko Y., Shilin I., Stanislav Minkov, Russian Journal of Mathematical Physics 2026 Vol. 33 No. 1 P. 89–106

In this paper, new numerical invariants of structurally unstable vector fields in the plane are found. One of the main tools is an improved asymptotics of sparkling saddle connections that occur when a separatrix loop of a hyperbolic saddle breaks. Another main tool is a new topological invariant of two arithmetic progressions, both perturbed and unperturbed, on the ...

Added: May 26, 2026

Comparative Study of Training Methods and Architectures of Echo State Networks

Androsov I., Proceedings of the Institute for System Programming of the RAS 2026 Vol. 38 No. 3 P. 87–114

This paper examines echo state networks (ESNs), one of the most prevalent approaches to implementing reservoir computing. An ESN consists of a recurrent neural network with fixed (untrained) weights and a readout layer that is typically linear and trainable. This approach enables the creation of energyefficient and computationally efficient neural networks capable of real-time learning. However, since ...

Added: May 26, 2026

ADDITIVE AUTOMORPHISMS OF REGULAR MATRIX GRAPH

Gusev I., Maksaev A., Promyslov V., Journal of Mathematical Sciences 2025 Vol. 299 No. 6

The regular graph of the space of n × m matrices over a field F is defined as the undirected graph whose vertices are matrices of rank min(n, m), and distinct matrices A and B are connected by an edge if and only if rk(A + B) < min(n, m). In this paper, for |F| ...

Added: May 25, 2026

Рефакторинг исходного кода на основе LLM и расширения UML

Караваева Е. А., Кулигин Л. А., Rezunik L. et al., Труды Института системного программирования РАН 2026 Т. 38 № 3 С. 67–94

В статье представлен метод рефакторинга исходного кода на основе интеграции большой языковой модели (LLM) и расширенной UML-модели программного кода. Предложенный подход позволяет выявлять проблемные участки кода с использованием функций тревожности и структурных метрик классов, а затем выполнять автоматизированный рефакторинг. Ключевой особенностью метода является использование LLM для генерации формальных спецификаций на языке OCL (Object Constraint Language), ...

Added: May 24, 2026

Coping with AI errors with provable guarantees

Tyukin I., Tyukina T., van Helden D. P. et al., Information Sciences 2024 Vol. 678 Article 120856

AI errors pose a significant challenge, hindering real-world applications. This work introduces a novel approach to cope with AI errors using weakly supervised error correctors that guarantee a specific level of error reduction. Our correctors have low computational cost and can be used to decide whether to abstain from making an unsafe classification. We provide ...

Added: May 23, 2026

Overcoming the Curse of Dimensionality with Synolitic AI

Zaikin A., Sviridov I., Sosedka A. et al., Technologies 2026 Vol. 14 No. 2 Article 84

High-dimensional tabular data are common in biomedical and clinical research, yet conventional machine learning methods often struggle in such settings due to data scarcity, feature redundancy, and limited generalization. In this study, we systematically evaluate Synolitic Graph Neural Networks (SGNNs), a framework that transforms high-dimensional samples into sample-specific graphs by training ensembles of low-dimensional pairwise ...

Added: May 23, 2026

Stable On-the-Fly Learning for Dynamic Neural Networks With Delayed Inputs

Chertopolokhov V., Mukhamedov A., Bugriy G. et al., IEEE Access 2026 Vol. 14 P. 14369–14392

This study presents on-the-fly identification and multi-step prediction of nonlinear systems with delayed inputs using a dynamic neural network combined with a smooth projection onto ellipsoids. The projection enforces parameter constraints that guarantee stability, while a Lyapunov–Krasovskii analysis yields computable ultimate error bounds. Riccati-type matrix inequalities are derived, providing an efficient vectorization–projection–devectorization implementation suitable for ...

Added: May 22, 2026

Analysis of the alternating minimization method for low-rank canonical polyadic decomposition in the Chebyshev norm

Морозов С. В., Calcolo 2026 Vol. 63 No. 2 Article 23

The approximation of tensors in a low-para metric format is a crucial component in many mathematical modelling and data analysis tasks. Among the widely used low-parametric representations, the canonical polyadic (CP) decomposition is known to be very efficient. Nowadays, most algorithms for CP approximation aim to construct the approximation in the Frobenius norm; however, some ...

Added: May 22, 2026

ML-based Fast Simulation of FARICH Responses

Shipilov F., Barnyakov A., Ivanov A. et al., / Series Physics "arxiv.org". 2026.

A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern particle physics simulation software but are computationally expensive. We present a machine-learning-based approach to fast simulation of the Focusing Aerogel Ring Imaging Cherenkov (FARICH) detector response. Given a particle track and momentum, ...

Added: May 19, 2026

Natural hazard database from Internet publications: text mining with a large language model

Derkacheva A., Sakirkina M., Kraev G. et al., /. 2026.

Comprehensive data on natural hazards and their consequences are crucial for effective for risk assessment, adaptation planning, and emergency response. However, many countries face challenges with fragmented, inconsistent, and inaccessible data, particularly regarding local-scale events. To address this data gap in Russia, we developed an end-to-end processing pipeline that scrapes news from various online sources, ...

Added: April 28, 2026

Algorithmic overlaps as thermodynamic variables: from local to cluster Monte Carlo dynamics in critical phenomena

Pilé I., Deng Y., Shchur L., / Series arXiv "math". 2026. No. 2604.10254.

We investigate the spatial overlap of successive spin configurations in Markov chain Monte Carlo simulations using the local Metropolis algorithm and the Svendsen-Wang and Wolff cluster algorithms. We examine the dynamics of these algorithms for two models in different universality classes: the Ising model and the Potts model with three components. The overlap of two ...

Added: April 20, 2026

Using predefined vector systems to speed up neural network multimillion class classification

Gabdullin N., Androsov I., / Series Computer Science "arxiv.org". 2026.

Label prediction in neural networks (NNs) has O(n) complexity proportional to the number of classes. This holds true for classification using fully connected layers and cosine similarity with some set of class prototypes. In this paper we show that if NN latent space (LS) geometry is known and possesses specific properties, label prediction complexity can ...

Added: April 2, 2026

Iterative Ricci-Foster Curvature Flow with GMM-Based Edge Pruning: A Novel Approach to Community Detection

Sorokin K., Beketov M., Онучин А. et al., / arxiv.org. Серия cs.SI "Social and Information Networks ". 2025.

Community detection in complex networks is a fundamental problem, open to new approaches in various scientific settings. We introduce a novel community detection method, based on Ricci flow on graphs. Our technique iteratively updates edge weights (their metric lengths) according to their (combinatorial) Foster version of Ricci curvature computed from effective resistance distance between the ...

Added: January 15, 2026

Implementing Transport Coding in OMNeT++ for Message Delay Reduction

Petrovanov I., Sergeev A., / Series Computer Science "arxiv.org". 2025. No. 2512.18332.

Transport coding reduces message delay in packet-switched networks by introducing controlled redundancy at the transport layer: original packets are encoded into coded packets, and the message is reconstructed after the first successful deliveries, effectively shifting latency from the maximum packet delay to the -th order statistic. We present a concise, reproducible discrete-event implementation of transport coding in OMNeT++, including ...

Added: December 24, 2025

Hessian-based lightweight neural network for brain vessel segmentation on a minimal training dataset

Меньшиков И. А., Бернадотт А. К., Elvimov N. S., / Series arXie "Statistical mechanics". 2025.

Accurate segmentation of blood vessels in brain magnetic resonance angiography (MRA) is essential for successful surgical procedures, such as aneurysm repair or bypass surgery. Currently, annotation is primarily performed through manual segmentation or classical methods, such as the Frangi filter, which often lack sufficient accuracy. Neural networks have emerged as powerful tools for medical image ...

Added: December 1, 2025

Determining the boundary of dynamical chaos in the generalized Chirikov map via machine learning

Chernyshov D., Satanin A., Shchur L., / Series arXiv "math". 2025.

We investigate the boundary separating regular and chaotic dynamics in the generalized Chirikov map, an extension of the standard map with phase-shifted secondary kicks. Lyapunov maps were computed across the parameter space (K,K(α, τ)) and used to train a convolutional neural network (ResNet18) for binary classification of dynamical regimes. The model reproduces the known critical ...

Added: November 21, 2025

Эффективный алгоритм торговли на фондовом рынке: ретроспективный анализ, основанный на данных по S&P-500.

Rubchinskiy A., Chubarova D., / Series WP7 "Математические методы анализа решений в экономике, бизнесе и политике". 2025. No. WP7/2025/01.

The article examines one of the most famous examples of socio-economic systems, characterized by significant uncertainty – the S&P-500 stock market, where shares of 500 largest US companies are traded. No assumptions are made about the probabilistic characteristics of the stock market. A flexible algorithm for daily trading has been developed, based on both known fixed data ...

Added: November 9, 2025

Diffusion on language model embeddings for protein sequence generation

Meshchaninov V., Strashnov, P., Shevtsov A. et al., / Cornell University. Серия CoRR, arXiv:2403.03726 "Computing Research Repository,". 2025.

Protein design requires a deep understanding of the inherent complexities of the protein universe. While many efforts lean towards conditional generation or focus on specific families of proteins, the foundational task of unconditional generation remains underexplored and undervalued. Here, we explore this pivotal domain, introducing DiMA, a model that leverages continuous diffusion on embeddings derived ...

Added: October 5, 2025