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Institution

Moscow Institute of Physics and Technology

EducationDolgoprudnyy, Russia
About: Moscow Institute of Physics and Technology is a education organization based out in Dolgoprudnyy, Russia. It is known for research contribution in the topics: Laser & Large Hadron Collider. The organization has 8594 authors who have published 16968 publications receiving 246551 citations. The organization is also known as: MIPT & Moscow Institute of Physics and Technology (State University).


Papers
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Journal ArticleDOI
TL;DR: In this paper, the core shift in radio-loud active galactic nuclei (AGNs) is studied and the relative magnetic field changes with frequency due to synchrotron self-absorption.
Abstract: The apparent position of jet base (core) in radio-loud active galactic nuclei changes with frequency because of synchrotron self-absorption. Studying this `core shift` effect enables us to reconstruct properties of the jet regions close to the central engine. We report here results from core shift measurements in AGNs observed with global VLBI at 2 and 8 GHz at epochs from 1994 to 2016. Our sample contains 40 objects observed at least 10 times during that period. The core shift is determined using a new automatic procedure introduced to minimize possible biases. The resulting multiple epoch measurements of the core position are employed for examining temporal variability of the core shift. We argue that the core shift variability is a common phenomenon, as established for 33 of 40 AGNs we study. Our analysis shows that the typical offsets between the core positions at 2 and 8 GHz are about 0.5 mas and they vary in time. Typical variability of the individual core positions is about 0.3 mas. The measurements show a strong dependence between the core position and its flux density, suggesting that changes in both are likely related to the nuclear flares injecting denser plasma into the flow. We determine that density of emitting relativistic particles significantly increases during these flares, while relative magnetic field changes less and in the opposite direction.

71 citations

Journal ArticleDOI
TL;DR: In this paper, the hidden structure of the answer for the HOMFLY polynomial for the figure-8 and some other 3-strand knots in representation is described.

71 citations

Journal ArticleDOI
TL;DR: The Mobile Identity Document Video dataset (MIDV-500) as mentioned in this paper is a collection of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems.
Abstract: A lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity document recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems. The paper presents characteristics of the dataset and evaluation results for existing methods of face detection, text line recognition, and document fields data extraction. Since an important feature of identity documents is their sensitiveness as they contain personal data, all source document images used in MIDV-500 are either in public domain or distributed under public copyright licenses. The main goal of this paper is to present a dataset. However, in addition and as a baseline, we present evaluation results for existing methods for face detection, text line recognition, and document data extraction, using the presented dataset.

71 citations

Journal ArticleDOI
TL;DR: This review can be useful for engineers and scientists who are using numerical simulation of the thermo-gasdynamics of combustion processes for the purpose of validating the developing computer codes and kinetic models.

71 citations

Journal ArticleDOI
16 Apr 2015
TL;DR: In this paper, a detailed comparison of various machine learning classifier algorithms, e.g., AdaBoost, MatrixNet and neural networks, was carried out to optimize the topological trigger for LHC Run 2.
Abstract: The main b-physics trigger algorithm used by the LHCb experiment is the so- called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the decay of a b-hadron. In the LHC Run 1, this trigger, which utilized a custom boosted decision tree algorithm, selected a nearly 100% pure sample of b-hadrons with a typical efficiency of 60-70%; its output was used in about 60% of LHCb papers. This talk presents studies carried out to optimize the topological trigger for LHC Run 2. In particular, we have carried out a detailed comparison of various machine learning classifier algorithms, e.g., AdaBoost, MatrixNet and neural networks. The topological trigger algorithm is designed to select all 'interesting" decays of b-hadrons, but cannot be trained on every such decay. Studies have therefore been performed to determine how to optimize the performance of the classification algorithm on decays not used in the training. Methods studied include cascading, ensembling and blending techniques. Furthermore, novel boosting techniques have been implemented that will help reduce systematic uncertainties in Run 2 measurements. We demonstrate that the reoptimized topological trigger is expected to significantly improve on the Run 1 performance for a wide range of b-hadron decays.

71 citations


Authors

Showing all 8797 results

NameH-indexPapersCitations
Dominique Pallin132113188668
Vladimir N. Uversky13195975342
Lee Sawyer130134088419
Dmitry Novikov12734883093
Simon Lin12675469084
Zeno Dixon Greenwood126100277347
Christian Ohm12687369771
Alexey Myagkov10958645630
Stanislav Babak10730866226
Alexander Zaitsev10345348690
Vladimir Popov102103050257
Alexander Vinogradov9641040879
Gueorgui Chelkov9332141816
Igor Pshenichnov8336222699
Vladimir Popov8337026390
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202368
2022238
20211,774
20202,246
20192,112
20181,902