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Observation of sizeable ω contribution to χc1(3872)→π+π−J/ψ decays
Physical Review D - Particles, Fields, Gravitation and Cosmology. 2023. Vol. 108. No. 1. Article L011103.
Aaij R., Abdelmotteleb A. S., Abellan Beteta C., Abudinén F., Ackernley T., Boldyrev A., Hushchyn M., Karpov M., Mokhnenko S., Ratnikov F., Ryzhikov A.
Aleksei Samarin, Nazarenko A., Nazarenko D. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 10 Article 330
Automated microscopic image analysis is essential for quantitative microbiology, yet manual acquisition and generic recognition models struggle with morphological variability and scene artifacts. We address both ends of this pipeline. First, we present a low-cost, open-source acquisition system, based on an Arduino UNO R3 microcontroller and NEMA 17 stepper motors, that automates focusing, magnification, and ...
Added: October 14, 2026
Mamedov I., Fofanov M., Lukianchenko P. et al., IEEE Access 2026 No. 14 P. 26662–26671
DreamBooth-style personalization, which often relies on one or a few images, can memorize context (background, pose, lighting) rather than learning the subject’s identity, thereby reducing fidelity and limiting prompt-driven edits. P-LoRA (Probability LoRA) is introduced as a training-time modification that biases diffusion timestep sampling and utilizes importance weights, ensuring that the overall objective remains unchanged. ...
Added: October 14, 2026
Springer, 2026.
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the XIV International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2025). The carefully selected papers cover ...
Added: October 13, 2026
Springer, 2026.
This book offers a comprehensive overview of the latest research, innovations, and practical applications in the field of sustainability, with a focus on construction materials and their use in building and civil engineering structures. As an outcome of the 3rd International Conference on Sustainability: Developments and Innovations (ICSDI 2026), this book presents a selection of ...
Added: October 13, 2026
Dmitriev A., Kornilov V., Batienkov R., Lecture Notes in Civil Engineering 2026 Vol. 886 P. 249–255
Numerous studies have provided compelling evidence that the probability distribution of avalanche sizes follows a power law, and that 1/f noise is present in the stochastic dynamics of landslides. We present a theoretical explanation of these phenomena within the framework of self-organized criticality theory, proposing a discrete-time model in the form of a sandpile cellular ...
Added: October 13, 2026
Dmitriev A., Shulman I., Lebedev A., Studies in Computational Intelligence 2026 Vol. 1265 P. 266–273
A discrete-time model is presented for the self-organization of the X/Twitter social network to the edge of a phase transition, characterized by avalanche-like propagation of information in the network. The model is based on the analogy between the critical dynamics of the X social network and sandpile cellular automata with Manna rule on the Chung-Lu ...
Added: October 13, 2026
Arutyunov K., Kharitonov I. A., Petrosyants K. O., JETP Letters 2026 Vol. 124 No. 6 P. 1–16
Superconducting properties of metallic micro- and nanowires can be entirely different from those of bulk superconductors. Of particular interest is the nonequilibrium current-induced state of a quasi-one-dimensional superconducting channel. The most interesting phenomenon here is the stepped I–V characteristic, explained by a process known as phase slip center activation. If the origin of such step-like ...
Added: October 13, 2026
Makarov D. M., Kalikin N., Fashchevsky K. A. et al., Electrochimica Acta 2026 Vol. 579 Article 150159
Machine learning (ML) has become a powerful tool for accelerating the development of carbon-based supercapacitors;
however, most published models assume that a single predictive framework can describe both conventional
porous carbons and hybrid carbon electrodes. Here, we test this assumption using a comprehensive
benchmark dataset and an extended literature-curated dataset enriched with synthesis- and electrolyte-related
descriptors. Three ML algorithms ...
Added: October 13, 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
Aaij R., Abdelmotteleb A. S., Abellan Beteta C. et al., Chinese Physics C 2023 Vol. 47 No. 9 Article 093001
https://iopscience.iop.org/article/10.1088/1674-1137/ace9c8#artAbst ...
Added: December 4, 2023
Aaij R., Abdelmotteleb A. S., Abellan Beteta C. et al., Physical Review Letters 2023 Vol. 131 No. 4 Article 042302
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.131.042302 ...
Added: December 4, 2023
Aaij R., Abdelmotteleb A. S., Abellan Beteta C. et al., Journal of High Energy Physics 2023 Vol. 2023 Article 81
https://link.springer.com/article/10.1007/JHEP04(2023)081#Abs1 ...
Added: December 4, 2023
Aaij R., Abdelmotteleb A. S., Abellan Beteta C. et al., Physical Review D - Particles, Fields, Gravitation and Cosmology 2023 Vol. 131 No. 10 Article 102301
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.131.102301 ...
Added: December 4, 2023