Zobrazeno 1 - 10
of 10 085
pro vyhledávání: '"Kiselev P"'
Autor:
Moioli, M., Popova, M. M., Hamilton, K. R., Ertel, D., Busto, D., Makos, I., Kiselev, M. D., Yudin, S. N., Ahmadi, H., Schröter, C. D., Pfeifer, T., Moshammer, R., Gryzlova, E. V., Grum-Grzhimailo, A. N., Bartschat, K., Sansone, G.
Attosecond photoelectron interferometry based on the combination of an attosecond pulse train and a synchronized infrared field is a fundamental technique for the temporal characterization of attosecond waveforms and for the investigation of electron
Externí odkaz:
http://arxiv.org/abs/2410.04240
Nambu-determinant brackets on $R^d\ni x=(x^1,...,x^d)$, $\{f,g\}_d(x)=\rho(x) \det(\partial(f,g,a_1,...,a_{d-2})/\partial(x^1,...,x^d))$, with $a_i\in C^\infty(R^d)$ and $\rho\partial_x\in\mathfrak{X}^d(R^d)$, are a class of Poisson structures with (
Externí odkaz:
http://arxiv.org/abs/2409.18875
Kontsevich constructed a map between `good' graph cocycles $\gamma$ and infinitesimal deformations of Poisson bivectors on affine manifolds, that is, Poisson cocycles in the second Lichnerowicz--Poisson cohomology. For the tetrahedral graph cocycle $
Externí odkaz:
http://arxiv.org/abs/2409.15932
Kontsevich constructed a map from suitable cocycles in the graph complex to infinitesimal deformations of Poisson bi-vector fields. Under the deformations, the bi-vector fields remain Poisson. We ask, are these deformations trivial, meaning, do they
Externí odkaz:
http://arxiv.org/abs/2409.12555
Autor:
Kiselev, Nikita, Grabovoy, Andrey
The loss landscape of neural networks is a critical aspect of their training, and understanding its properties is essential for improving their performance. In this paper, we investigate how the loss surface changes when the sample size increases, a
Externí odkaz:
http://arxiv.org/abs/2409.11995
Autor:
Kiselev, Mikhail
In the present paper, it is shown how the columnar/layered CoLaNET spiking neural network (SNN) architecture can be used in supervised learning image classification tasks. Image pixel brightness is coded by the spike count during image presentation p
Externí odkaz:
http://arxiv.org/abs/2409.07833
Autor:
Kiselev, Mikhail
In the present paper, I describe a spiking neural network (SNN) architecture which, can be used in wide range of supervised learning classification tasks. It is assumed, that all participating signals (the classified object description, correct class
Externí odkaz:
http://arxiv.org/abs/2409.01230
We investigate Lorentz invariance breaking in quantum wires due to Rashba spin-orbit interaction and transverse phonons. Using bosonization, we derive an effective action coupling electronic and mechanical degrees of freedom. Stikingly, at a quantum
Externí odkaz:
http://arxiv.org/abs/2407.08613
Autor:
Kiselev, Georgiy
This paper demonstrates the efficacy of a modified U-Net structure for the extraction of vascular tree masks for human fundus photographs. On limited compute resources and training data, the proposed model only slightly underperforms when compared to
Externí odkaz:
http://arxiv.org/abs/2407.04940
We study penetration of interstellar cosmic rays (CRs) into molecular clouds surrounded by nonuniform diffuse envelopes. The present work generalizes our earlier model of CR self-modulation (Ivlev et al. 2018, Dogiel et al. 2018), in which the value
Externí odkaz:
http://arxiv.org/abs/2407.03998