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Direct CP violation in charmless three-body decays of B± mesons
Physical Review D - Particles, Fields, Gravitation and Cosmology. 2023. Vol. 108. No. 1. Article 012008.
Aaij R., Abdelmotteleb A. S., Abellan Beteta C., Abudinén F., Ackernley T., Boldyrev A., Hushchyn M., Karpov M., Mokhnenko S., Ratnikov F., Ryzhikov A.
Neal N. X., Weiqing L., Dacheng H. et al., Algorithms 2026 Vol. 19 No. 5 P. 1–22
In the era of data-driven education, educational social networks generate large volumes of high-dimensional and complex-structured data through learner interactions, collaborative activities, and resource-sharing behaviors, posing significant challenges to traditional unsupervised learning methods. Such data often exhibit non-convex distributions, heterogeneity, and noise sensitivity, making conventional clustering approaches insufficient for capturing their intrinsic structural relationships. To ...
Added: May 13, 2026
Velichkov B., Nikolova-Koleva I., Slavcheva M., Shumen: INCOMA Ltd, 2025.
The RANLP 2025 Student Research Workshop (RANLPStud’2025) is a special track of the established international conference Recent Advances in Natural Language Processing (RANLP’2025).
The RANLPStud is being organised for the 9th time and this year is running in parallel with the other tracks of the main RANLP 2025 conference. The target of RANLPStud’25 is to be a ...
Added: May 12, 2026
Stepanyants V., Долгов И. М., Хорошилов Г. С. et al., Труды Института системного программирования РАН 2026 Т. 38 № 3 С. 95–110
Highly automated and connected vehicles are gradually entering the market. Currently, solutions are being proposed that allow these technologies to be used for cooperative driving automation, which can significantly improve traffic safety. Such technologies and their software should be tested to ensure safety before being implemented in real systems. Verification and validation of vehicular control ...
Added: May 12, 2026
Tikhonov R., Efendiev M. T., Fedotenkov A. A., 2026 International Russian Smart Industry Conference (SmartIndustryCon) 2026 P. 542–547
High-fidelity simulation environments like CARLA and ROS are essential for connected and automated vehicle research. They allow researchers to verify and validate new software and technology without the time, financial, and safety overheads of real-world testing. However, their operation requires considerable expertise for creating platform-specific scenario configuration files, which complicates the research workflow. This paper ...
Added: May 11, 2026
Bisnovatyi-Kogan G., Kondratyev I., Moiseenko S. G., International Journal of Modern Physics A 2025 Vol. 40 No. 7 Article 2550018
Bright transient objects in different wave bands have been discovered in recent years.
To explain these short (from ms to s), and very powerful events, different models, galactic and
extragalactic, have been considered. One of the popular models is based on the suggestion of
transformation of the magnetized plasma blob, presumably a magnetohydrodynamic (MHD)
shock wave, moving with relativistic ...
Added: May 11, 2026
Торопина О. Д., Бисноватый-Коган Г. С., Moiseenko S. G., Astronomy Reports 2025 Vol. 69 No. Suppl. 1 P. 80–90
The results of MHD simulations of supersonic astrophysical and laboratory jets in an external
poloidal magnetic field (Br,Bz) taking into account the rotation of the matter, are presented. The ejected
matter is collimated by the magnetic field, the degree of collimation and the flow structure depend on the
relation between of the magnetic field induction and the angular ...
Added: May 11, 2026
Los Alamitos: IEEE Computer Society, 2026.
It is a great pleasure for us to welcome you on behalf of the conference committees, to the 11th IEEE International Conference on Smart Cloud (IEEE SmartCloud 2026), we are glad that we can have this international conference in New York city, USA. Now, please allow us to introduce the IEEE SmartCloud 2026 conference. The ...
Added: May 10, 2026
Avdoshin S. M., Pesotskaya E. Y., Информационные технологии 2026 Т. 32 № 4 С. 185–194
With the rapid advancement of artificial intelligence, and deep learning in particular, models have emerged that are capable of delivering highly accurate predictions. However, the internal logic of such models remains difficult to interpret—an issue of critical importance, especially in domains where the correctness of an algorithm directly affects high-stakes decision-making. One promising avenue for ...
Added: May 8, 2026
Avdoshin S. M., Pesotskaya E. Y., Business Informatics 2026 Vol. 20 No. 1 P. 7–28
The rapid development of artificial intelligence (AI) is accompanied by increasing computational
complexity and decreasing model transparency, which significantly limits its adoption in critical
domains that require a high level of trust, interpretability, and justification of decisions. Under these
conditions, the field of Explainable Artificial Intelligence (XAI) has gained particular importance as it
focuses on approaches and technologies that ...
Added: May 8, 2026
Varnavsky A., IEEE Access 2026 Vol. 14 P. 37487–37508
Puzzles are an excellent tool for learning computer science and programming, fostering increased interest, engagement, and motivation among students, as well as developing logical, critical, and computational thinking. Among beginner programmers, Parson's Programming Puzzles are quite popular, aimed at mastering the basic syntactic and logical constructs of programming languages. However, as students' skills grow, their ...
Added: May 7, 2026
Ismagilov T., Mukosey A., Smirnov F. et al., International Journal of High Performance Computing Applications 2026 Vol. 40 No. 2 P. 240–253
One of the most important aspects of supercomputer development in the post-Moore era is the interconnect technologies that allow one to unite a multitude of processing elements into a well-synchronized computing system. Novel types of supercomputer interconnect require careful benchmarking and compliance with the requirements of modern hardware trends. GPU-based heterogeneous computing is one of ...
Added: May 7, 2026
Yasnitsky L., Голдобин М. А., Мезенцев А. С., Прикладная математика и вопросы управления 2025 № 2 С. 99–116
Представлен обзор современных методов и основанных на них программных
инструментах, применяемых для математического моделирования серийных производственных процессов с целью снижения брака и повышения качества производимых изделий. Перечисляются группы работ, нацеленных на обнаружение и классификацию дефектов, работ, в которых решаются задачи прогнозирования образования дефектов и определения значимости параметров, работ направленных на поиск
оптимального сочетания технологических параметров изготовления изделий, ...
Added: May 5, 2026
Morozov E. V., Demin A. S., Borovitskaya I. V. et al., Inorganic Materials: Applied Research 2026 Vol. 17 No. 3 P. 619–626
This article presents the results of experiments on the impact of powerful pulsed ion-plasma and
electron flows generated in the working chamber of the Plasma Focus PF-5M installation during each highvoltage
discharge on a model aluminum alloy B95. It is shown that the general characteristics of the alloy’s
damage upon exposure to pulsed flows of helium ions (HI) ...
Added: May 4, 2026
Монахова Э. А., Монахов О. Г., Rzaev E. et al., Прикладная дискретная математика 2026 Т. 71 С. 112–127
В настоящей работе исследовано совместное конструирование топологий семейств оптимальных по диаметру циркулянтных сетей $C(N; \pm 1, \pm s_2)$ и реализуемых для них оптимальных алгоритмов маршрутизации сложности $O(1)$. Предлагаемый алгоритм маршрутизации основан на использовании масштабируемых параметров $L$-образных шаблонов плотной укладки графов на плоскости для семейств оптимальных сетей.
Определены аналитические формулы зависимости этих параметров от диаметра графов семейств ...
Added: May 4, 2026
Sosnin E. I., Vasil’ev Y. L., Solovyev R. A. et al., Computer Optics 2025 Vol. 49 No. 6 P. 1129–1137
In this article, we present a new unique dataset for dental research – AlphaDent. This dataset is based on the DSLR camera photographs of the teeth of 295 patients and contains over 1200 images. The dataset is labeled for solving the instance segmentation problem and is divided into 9 classes. The article provides a detailed ...
Added: May 4, 2026
Назаренко А. Г., Федоров М. В., Moshkin A. et al., Вестник Росздравнадзора 2026 № 1 С. 14–29
Multimodal foundation models and medical multimodal large language models are establishing a new class of diagnostic clinical decision support systems capable of operating on heterogeneous data sources, including medical imaging (X-ray, CT, MRI, ultrasound, histopathology), physiological signals (ECG, EEG), clinical text (electronic health records, reports, discharge summaries), laboratory measurements, molecular profiling data, and related modalities. ...
Added: May 4, 2026
Honolulu: IEEE, 2025.
International Conference on Computer Vision Workshops (ICCVW), Honolulu, HI, USA, 2025 ...
Added: May 3, 2026
Vasilev A., Kapitanov A., Solovyev Roman A. et al., PeerJ Computer Science 2026 Vol. 12 Article 3724
This article introduces MinMAE, a novel activation calibration method for Post-Training Quantization (PTQ) that significantly reduces accuracy loss in Convolutional Neural Networks (CNN). Motivated by the need for high-fidelity quantization without costly retraining, MinMAE directly minimizes the Mean Absolute Error (MAE) between original and dequantized activations, making it robust to outliers that degrade standard methods. ...
Added: May 3, 2026
Solovyev Roman A., Telpukhov Dmitry, Shafeev I. et al., Technologies 2026 Vol. 14 No. 3 Article 169
With the continuous scaling of semiconductor design technologies, evaluating static IR drop has become a critical bottleneck in the physical synthesis flow. This paper presents a machine learning-based framework that transforms the power delivery network (PDN) analysis problem into an image-to-image translation task using a U-Net architecture with MaxViT and EfficientNet encoders. By implementing a ...
Added: May 3, 2026
Aehle M., Arsini L., Belén Barreiro R. et al., Reviews in Physics 2025 Vol. 13 Article 100120
In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance — from the data-generating processes to their reconstruction and ...
Added: June 15, 2025
Derkach D., Kazeev N., Mokhnenko S. et al., EPJ Web of Conferences 2024 Vol. 295 P. 03040
Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, ...
Added: January 8, 2025
Ali S., Ryzhikov A., Derkach D. et al., Moscow University Physics Bulletin 2024 Vol. 79 No. Suppl. 2 P. S591–S597
In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorimeter data due to aging or other factors. Leveraging the Wasserstein ...
Added: November 7, 2024
Rogachev A., Ratnikov F., Computing and Software for Big Science 2024 Vol. 8 No. 1 Article 12
In this paper, we explore the use of Generative Adversarial Networks (GANs) to speed up the simulation process while ensuring that the generated results are consistent in terms of physics metrics. Our main focus is the application of spectral normalization for GANs to generate electromagnetic calorimeter (ECAL) response data, which is a crucial component of ...
Added: July 2, 2024
Rogachev A., Ratnikov F., EPJ Web of Conferences 2024 Vol. 295 Article 09007
High energy physics experiments heavily rely on the results of MC simulation of data used to extract physics results. However, the detailed simulation often requires tremendous amount of computation resources.
Using Generative Adversarial Networks and other deep learning generative techniques can drastically speed up the computationally heavy simulations like a simulation of the calorimeter response. To ...
Added: May 20, 2024