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pro vyhledávání: '"P. Sergei"'
This article extends the study of dynamical properties of the symmetric McMillan map, emphasizing its utility in understanding and modeling complex nonlinear systems. Although the map features six parameters, we demonstrate that only two are irreduci
Externí odkaz:
http://arxiv.org/abs/2410.10380
We prove that the Kontsevich graph complex $GC_d^{2}$ and its oriented version $OGC_{d+1}^2$ are quasi-isomorphic as dg Lie algebras.
Comment: 10 pages
Comment: 10 pages
Externí odkaz:
http://arxiv.org/abs/2411.19657
Autor:
Agapov, Sergei
We study Riemannian metrics on 2-surfaces with integrable geodesic flows such that an additional first integral is high-degree polynomial in momenta. This problem reduces to searching for solutions to certain quasi-linear systems of PDEs which turn o
Externí odkaz:
http://arxiv.org/abs/2411.18920
Autor:
Belov, Danil, Erkhov, Artem, Khabibullin, Farit, Pestova, Elisaveta, Satsevich, Sergei, Osokin, Ilya, Osinenko, Pavel, Tsetserukou, Dzmitry
This paper is dedicated to the development of a novel adaptive torsion spring mechanism for optimizing energy consumption in legged robots. By adjusting the equilibrium position and stiffness of the spring, the system improves energy efficiency durin
Externí odkaz:
http://arxiv.org/abs/2411.18295
Non-reciprocal systems can be thought of as disobeying Newtons third law - an action does not cause an equal and opposite reaction. In recent years there has been a dramatic rise in interest towards such systems. On a fundamental level, they can be a
Externí odkaz:
http://arxiv.org/abs/2411.17944
The quest to develop an effective string theory capable of describing the confining flux tube has been a longstanding objective within the theoretical physics community. Recent lattice results indicate that the low-lying spectrum of the flux tube in
Externí odkaz:
http://arxiv.org/abs/2411.16507
Autor:
Dinitz, Michael, Im, Sungjin, Lavastida, Thomas, Moseley, Benjamin, Niaparast, Aidin, Vassilvitskii, Sergei
Algorithms with (machine-learned) predictions is a powerful framework for combining traditional worst-case algorithms with modern machine learning. However, the vast majority of work in this space assumes that the prediction itself is non-probabilist
Externí odkaz:
http://arxiv.org/abs/2411.16030
The rapid progress in machine learning models has significantly boosted the potential for real-world applications such as autonomous vehicles, disease diagnoses, and recognition of emergencies. The performance of many machine learning models depends
Externí odkaz:
http://arxiv.org/abs/2411.15602
Autor:
Neto, Jogi Suda, Forestano, Roy T., Gleyzer, Sergei, Kong, Kyoungchul, Matchev, Konstantin T., Matcheva, Katia
Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop a Lie-Equi
Externí odkaz:
http://arxiv.org/abs/2411.15315
In this work, we consider instantaneous transitions of an infinitely extended uniaxial dielectric into a wire medium (WM) of continuous infinitely long conducting wires. Due to the strong spatial dispersion in the WM the known (Morgenthaler's) theory
Externí odkaz:
http://arxiv.org/abs/2411.14805