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Optimization of List-Based Reed-Solomon Decoding via Deep Learning
P. 1–4.
This paper presents a deep learning-based optimization of a list-decoding algorithm for a concatenated Hamming-Reed-Solomon code transmitted over an AWGN channel with BPSK modulation. The proposed method reformulates early termination of the erasure-pattern list as a weighted regression problem, addressing class imbalance through an asymmetric Weighted Mean Squared Error (WMSE) loss function that penalizes index underestimation more heavily than overestimation. Simulation results confirm that the neural network predictor reduces the average number of decoding iterations across the evaluated SNR range while maintaining error-correction performance comparable to the baseline list decoder.
In book
Petrozavodsk: IEEE, 2026.
Rozhdestvenskaya E., Abramov R., Журнал социологии и социальной антропологии 2026 Т. 29 № 3 С. 244–274
The article presents an experimental study at the intersection of social anthropology and qualitative sociology. The empirical basis is a corpus of 18 short biographical sketches of collective farm peasant women from the village of Vysheley, Penza Region, created at the end of the Soviet period by school local historians and discovered by one of the authors during a ...
Added: October 7, 2026
Aleksei Samarin, Nazarenko A., Alexander Savelev et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 3 P. 844–854
Improving image quality is becoming an increasingly popular task, especially when working with mobile devices. One common approach to image enhancement is the use of convolutional neural networks. However, to achieve good results, such networks must be large enough, otherwise there is a risk of unwanted artifacts. In addition, large convolutional neural networks require significant ...
Added: September 21, 2026
Самарин А. В., Торопов А. Г., Савельев А. Г. et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 4 P. 1044–1052
This paper presents a study aimed at improving the detection quality of small-sized microorganisms under challenging microscopic conditions through the application of a lightweight combined image preprocessing model. We focused on the task of detecting diplococci in images obtained through dynamic sample microscopy. The proposed approach employs predefined filters for image preprocessing, combined with the ...
Added: September 21, 2026
Singapore: Springer, 2025.
Added: September 21, 2026
Aleksei Samarin, Nazarenko A., Kotenko E. et al., / Series arXiv "math". 2025. No. 2511.20141.
This paper presents a novel approach to neural network compression that addresses redundancy at both the filter and architectural levels through a unified framework grounded in information flow analysis. Building on the concept of tensor flow divergence, which quantifies how information is transformed across network layers, we develop a two-stage optimization process. The first stage ...
Added: September 21, 2026
Aleksei Samarin, Aleksei Toropov, Alexander Savelev et al., , in: Pattern Recognition. ICPR 2024 International Workshops and Challenges. Kolkata, India, December 1, 2024, Proceedings, Part III. LNCS, volume 15616.: Cham: Springer, 2025. P. 308–320.
This research explores an innovative approach to enhancing the accuracy of detecting small microorganisms in complex microscopic environments. Our study introduces a streamlined, hybrid image pre-processing model specifically designed to address the challenges of identifying diplococci in live microscopy of dynamic samples. By integrating pre-defined filtering techniques with predictive adjustments for optimal applicability, our method ...
Added: September 21, 2026
Cham: Springer, 2025.
Added: September 21, 2026
Cham: Springer, 2026.
computer vision ...
Added: September 19, 2026
Cham: Springer, 2026.
Added: September 19, 2026
FRUCT Oy, 2024.
Added: September 19, 2026
Aleksei Samarin, Aleksei Toropov, Dzestelova A. et al., , in: Proceedings of the 36th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 30 October - 1 November 2024Vol. 36.: FRUCT Oy, 2024. P. 903–911.
In this work, we describe our research aimed at developing classifiers for microbial images (bacilli images) obtained through microscopy of live (non-static) samples. We employed our proposed approach called AutoML, which is based on the automatic generation and analysis of the feature space to create the most optimal descriptors for microscopic images used in their ...
Added: September 19, 2026
FRUCT Oy, 2024.
Added: September 19, 2026
Helsinki: FRUCT Oy, 2025.
Added: September 19, 2026
Aleksei Samarin, Aleksei Toropov, Nazarenko A. et al., , in: Proceedings of FRUCT'39, Helsinki, Finland, 28-30 April 2026. Issue 1 (Full Papers)Vol. 39.: Helsinki: FRUCT Oy, 2026. P. 253–261.
Commercial facade service classification from streetlevel imagery benefits from multi-branch pipelines that fuse global facade appearance with signboard-centric cues; however, this design increases inference cost and hinders large-scale deployment. We study pruning for a multi-branch facade recognition pipeline with a trainable signboard descriptor and compare unstructured magnitude pruning with structured baselines, including channel pruning and ...
Added: September 19, 2026
Helsinki: FRUCT Oy, 2026.
Added: September 19, 2026
Aleksei Samarin, Kotenko E., Aleksei Toropov et al., , in: ICICT 2026: 2026 The 9th International Conference on Information and Computer Technologies Honolulu, HI, USA March 11 - 13, 2026.: NY: Association for Computing Machinery (ACM), 2026. P. 155–161.
This study investigates the integration of attention mechanisms within deep learning architectures, specifically focusing on the segmentation of diplococci microorganisms in microscopy images. We introduce novel modifications to attention blocks, tailored to address challenges inherent to microscopy imaging, such as blurred boundaries and indistinct microorganism features. Our experimental results demonstrate significant performance improvements compared to ...
Added: September 19, 2026
Aleksei Samarin, Nazarenko A., Kotenko E. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 8 P. 1–26
This paper presents a novel method for pruning deep neural networks based on the concept of flow, derived from the continuous modeling of signal propagation across layers. We derive flow functions for fully connected, convolutional, and self-attention architectures, and we propose a new iterative pruning algorithm, Iterative Flow-Aware Pruning (IFAP), that leverages these measures to ...
Added: September 19, 2026
Ramos-Soto O., Perez-Zarate E., Ramos-Frutos J. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 7 Article 173
Added: September 18, 2026
Lebedev O. B., Черкасов Р. И., Известия ЮФУ. Технические науки 2025 № 5(247) С. 254–276
This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are ...
Added: September 10, 2026
Lebedev O. B., Шмелева А. Г., Гежа Н. С., Информатика и автоматизация (Труды СПИИРАН) 2026 Т. 25 № 3 С. 720–750
This paper describes the development of a neural network model for automated analysis of medical data in electrophysiology based on deep learning methods. The relevance of this work stems from the growing need to improve the objectivity, speed, and accuracy of processing complex spatiotemporal signals, such as ECG or EEG. Convolutional neural networks (CNNs), which ...
Added: September 10, 2026
Vyacheslav D. Neverov, Lukyanov A., Andrey V. Krasavin et al., Physical Review B: Condensed Matter and Materials Physics 2026 Vol. 113 No. 2 Article 024515
We develop a neural network approach to solve the self-consistent Bogoliubov-de Gennes equations in strongly disordered s-wave superconductors. The method accurately reproduces inhomogeneous gap distributions and generalizes to system sizes far larger than those used in training. It reduces computational scaling from O(N6 ) to O(N2), enabling quantitative analysis of percolation phenomena and the superconductor-insulator ...
Added: September 5, 2026