Institution
Moscow Institute of Physics and Technology
Education•Dolgoprudnyy, 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).
Topics: Laser, Large Hadron Collider, Electron, Plasma, Magnetic field
Papers published on a yearly basis
Papers
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TL;DR: This paper introduces Exponential Machines (ExM), a predictor that models all interactions of every order in a factorized format called Tensor Train (TT), and shows that the model achieves state-of-the-art performance on synthetic data with high-order interactions and works on par on a recommender system dataset MovieLens 100K.
Abstract: Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we introduce Exponential Machines (ExM), a predictor that models all interactions of every order. The key idea is to represent an exponentially large tensor of parameters in a factorized format called Tensor Train (TT). The Tensor Train format regularizes the model and lets you control the number of underlying parameters. To train the model, we develop a stochastic Riemannian optimization procedure, which allows us to fit tensors with 2^160 entries. We show that the model achieves state-of-the-art performance on synthetic data with high-order interactions and that it works on par with high-order factorization machines on a recommender system dataset MovieLens 100K.
74 citations
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TL;DR: It is shown that the relative importance of each gene product in a pathway can be assessed using kinetic models for “low-level” protein interactions and showed that ignoring these factors can be sometimes acceptable and that the simplified formula for SPA evaluation may be applied for many cases.
Abstract: We propose a new biomathematical method, OncoFinder, for both quantitative and qualitative analysis of the intracellular signaling pathway activation (SPA). This method is universal and may be used for the analysis of any physiological, stress, malignancy and other perturbed conditions at the molecular level. In contrast to the other existing techniques for aggregation and generalization of the gene expression data for individual samples, we suggest to distinguish the positive/activator and negative/repressor role of every gene product in each pathway. We show that the relative importance of each gene product in a pathway can be assessed using kinetic models for "low-level" protein interactions. Although the importance factors for the pathway members cannot be so far established for most of the signaling pathways due to the lack of the required experimental data, we showed that ignoring these factors can be sometimes acceptable and that the simplified formula for SPA evaluation may be applied for many cases. We hope that due to its universal applicability, the method OncoFinder will be widely used by the researcher community.
74 citations
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TL;DR: In this article, Born cross sections for the three-body production of sigma(e(+)e(-) -> [B (B) over bar* + c.c.](+/-)pi(-/+)) were measured.
Abstract: We report the analysis of the three-body e(+)e(-) -> B (B) over bar pi(+/-), B (B) over bar*pi(+/-), and B*(B) over bar* pi(+/-) processes, including the first observations of the Z(b)(+/-)(10610) -> [B (B) over bar* + c.c.](+/-) and Z(b)(+/-)(10650) -> [B*(B) over bar*](+/-) transitions that are found to dominate the corresponding final states. We measure Born cross sections for the three-body production of sigma(e(+)e(-) -> [B (B) over bar* + c.c.](+/-)pi(-/+)) = [17.4 +/- 1.6(stat) +/- 1.9(syst)]pb and sigma(e(+)e(-) -> [B*(B) over bar*](+/-)pi(-/+)) = [8.75 +/- 1.15(stat) +/- 1.04(syst)] pb and set a 90% C.L. upper limit of sigma(e(+)e(-) -> [B (B) over bar](+/-)pi(-/+)) < 2.9 pb. The results are based on a 121.4 fb(-1) data sample collected with the Belle detector at a center-of-mass energy near the Upsilon(10860) peak.
74 citations
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Albert M. Sirunyan1, Robin Erbacher2, C. A. Carrillo Montoya3, Wagner Carvalho4 +2251 more•Institutions (149)
TL;DR: In this article, the nuclear modification factors of mesons were measured in collisions at the LHC at a center-of-mass energy per nucleon pair of $sqrt{\smash [b]{s_{\text {NN}}}}} = 5.02
Abstract: The nuclear modification factors of ${\mathrm {J}/\psi }$ and $\psi \text {(2S)}$ mesons are measured in $\text {PbPb}$ collisions at a centre-of-mass energy per nucleon pair of $\sqrt{\smash [b]{s_{_{\text {NN}}}}} = 5.02\,\text {Te}\text {V} $ . The analysis is based on $\text {PbPb}$ and $\mathrm {p}\mathrm {p}$ data samples collected by CMS at the LHC in 2015, corresponding to integrated luminosities of 464 $\,\mu \mathrm {b}^{-1}$ and 28 $\,\text {pb}^\text {-1}$ , respectively. The measurements are performed in the dimuon rapidity range of $|y | 25$ ${\,\text {Ge}\text {V}/}\text {c}$ is seen with respect to that observed at intermediate $p_{\mathrm {T}}$ . The prompt $\psi \text {(2S)}$ meson yield is found to be more suppressed than that of the prompt ${\mathrm {J}/\psi }$ mesons in the entire $p_{\mathrm {T}}$ range.
74 citations
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TL;DR: In this paper, the authors measured the charged particle distributions in proton-proton collisions at a center-of-mass energy of 13 TeV, using a data sample of nearly 9 million events, corresponding to an integrated luminosity.
74 citations
Authors
Showing all 8797 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dominique Pallin | 132 | 1131 | 88668 |
Vladimir N. Uversky | 131 | 959 | 75342 |
Lee Sawyer | 130 | 1340 | 88419 |
Dmitry Novikov | 127 | 348 | 83093 |
Simon Lin | 126 | 754 | 69084 |
Zeno Dixon Greenwood | 126 | 1002 | 77347 |
Christian Ohm | 126 | 873 | 69771 |
Alexey Myagkov | 109 | 586 | 45630 |
Stanislav Babak | 107 | 308 | 66226 |
Alexander Zaitsev | 103 | 453 | 48690 |
Vladimir Popov | 102 | 1030 | 50257 |
Alexander Vinogradov | 96 | 410 | 40879 |
Gueorgui Chelkov | 93 | 321 | 41816 |
Igor Pshenichnov | 83 | 362 | 22699 |
Vladimir Popov | 83 | 370 | 26390 |