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Institution

Russian Academy of Sciences

GovernmentMoscow, Russia
About: Russian Academy of Sciences is a government organization based out in Moscow, Russia. It is known for research contribution in the topics: Laser & Population. The organization has 272615 authors who have published 417512 publications receiving 4538835 citations. The organization is also known as: RAS & RAN.
Topics: Laser, Population, Catalysis, Magnetic field, Electron


Papers
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Proceedings ArticleDOI
21 Jul 2017
TL;DR: A unified implementation of the Faster R-CNN, R-FCN and SSD systems is presented and the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures is traced out.
Abstract: The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-toapples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [30], R-FCN [6] and SSD [25] systems, which we view as meta-architectures and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.

2,484 citations

Journal ArticleDOI
TL;DR: In this paper, the spin-orbit interaction Hamiltonian HSO = alpha ( sigma *k) was used to change the usual patterns of B-1-periodic oscillations; some oscillations are strongly suppressed due to the diminishing of the gaps between adjacent levels and new oscillations appear due to intersections of levels.
Abstract: Oscillatory effects in a strong magnetic field B and magnetic susceptibility are investigated, as applied to 2D systems, in which the twofold spin degeneracy is lifted by the spin-orbit-interaction Hamiltonian HSO= alpha ( sigma *k). nu . The term HSO is shown to change greatly the usual patterns of B-1-periodic oscillations; some oscillations are strongly suppressed due to the diminishing of the gaps between adjacent levels, and new oscillations appear due to intersections of levels.

2,390 citations

Journal ArticleDOI
TL;DR: Two types of nonlinear control algorithms are presented for uncertain linear plants, stabilizing polynomial feedbacks that allow to adjust a guaranteed convergence time of system trajectories into a prespecified neighborhood of the origin independently on initial conditions.
Abstract: Two types of nonlinear control algorithms are presented for uncertain linear plants. Controllers of the first type are stabilizing polynomial feedbacks that allow to adjust a guaranteed convergence time of system trajectories into a prespecified neighborhood of the origin independently on initial conditions. The control design procedure uses block control principles and finite-time attractivity properties of polynomial feedbacks. Controllers of the second type are modifications of the second order sliding mode control algorithms. They provide global finite-time stability of the closed-loop system and allow to adjust a guaranteed settling time independently on initial conditions. Control algorithms are presented for both single-input and multi-input systems. Theoretical results are supported by numerical simulations.

2,380 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present new weak lensing observations of 1E0657-558 (z = 0.296), a unique cluster merger, that enable a direct detection of dark matter, independent of assumptions regarding the nature of the gravitational force law.
Abstract: We present new weak lensing observations of 1E0657-558 (z = 0.296), a unique cluster merger, that enable a direct detection of dark matter, independent of assumptions regarding the nature of the gravitational force law. Due to the collision of two clusters, the dissipationless stellar component and the fluid-like X-ray emitting plasma are spatially segregated. By using both wide-field ground based images and HST/ACS images of the cluster cores, we create gravitational lensing maps which show that the gravitational potential does not trace the plasma distribution, the dominant baryonic mass component, but rather approximately traces the distribution of galaxies. An 8{sigma} significance spatial offset of the center of the total mass from the center of the baryonic mass peaks cannot be explained with an alteration of the gravitational force law, and thus proves that the majority of the matter in the system is unseen.

2,332 citations

Journal ArticleDOI
TL;DR: In this paper, the authors argue that the Higgs boson of the Standard Model can lead to inflation and produce cosmological perturbations in accordance with observations, and that the essential requirement is the non-minimal coupling of the scalar field to gravity; no new particle besides already present in the electroweak theory is required.

2,262 citations


Authors

Showing all 273043 results

NameH-indexPapersCitations
Eugene V. Koonin1991063175111
Martin Karplus163831138492
James M. Tiedje150688102287
Alexander Belyaev1421895100796
R. A. Sunyaev141848107966
Robert Huber13967173557
Jaap S. Sinninghe Damsté13472661947
Sergei Gninenko131124588640
Vladimir N. Uversky13195975342
Mikhail Kirsanov129122887573
Victor Kim129128787209
Christopher Bee12896080118
Martin Kirakosyan128116878323
Vladimir Smakhtin12886974383
Valery Schegelsky128107982072
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023164
2022859
202118,386
202023,163
201922,366
201820,365