CDISE: publications
Data retrieved from SciVal platform (Scopus database) on September 30, 2020.
Publications are categorized in accordance with lists of Academic personnel for each particular year.
2016
1-37
2017
38-106
2018
107-241
2019
242-484, S5, S13
2020 485-631, S1-S4, S6-S7, S9-S12, S14-S18
1. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.
(2016).Domain-adversarial training of neural networks.
Journal of Machine Learning Research,17
Scopus: 2-s2.0-84979887690

2. Lebedev, V., Lempitsky, V.
(2016).Fast ConvNets Using Group-Wise Brain Damage.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2016-2554-2564
DOI: 10.1109/CVPR.2016.280 Scopus: 2-s2.0-84986264846

3. Haegeman, J., Lubich, C., Oseledets, I., Vandereycken, B., Verstraete, F.
(2016).Unifying time evolution and optimization with matrix product states.
Physical Review B,94(16)
DOI: 10.1103/PhysRevB.94.165116
Scopus: 2-s2.0-84992146443

4. Ustinova, E., Lempitsky, V.
(2016).Learning deep embeddings with histogram loss.
Advances in Neural Information Processing Systems,4177-4185
Scopus: 2-s2.0-85019265802

5. Shapeev, A.V.
(2016).Moment tensor potentials: A class of systematically improvable interatomic potentials.
Multiscale Modeling and Simulation,14(3) 1153-1173
DOI: 10.1137/15M1054183
Scopus: 2-s2.0-84989347241

6. Cichocki, A., Lee, N., Oseledets, I., Phan, A.-H., Zhao, Q., Mandic, D.P.
(2016).Tensor networks for dimensionality reduction and large-scale optimization part 1 low-rank tensor decompositions.
Foundations and Trends in Machine Learning,9(4-5) 249-429
DOI: 10.1561/2200000059
Scopus: 2-s2.0-85007040135

7. Miller, O.D., Polimeridis, A.G., Reid, M.T.H., Hsu, C.W., Delacy, B.G., Joannopoulos, J.D., Soljačić, M., Johnson, S.G.
(2016).Fundamental limits to optical response in absorptive systems.
Optics Express,24(4) 3329-3364
DOI: 10.1364/OE.24.003329
Scopus: 2-s2.0-84961614137

8. Arteta, C., Lempitsky, V., Zisserman, A.
(2016).Counting in the wild.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),9911483-498
DOI: 10.1007/978-3-319-46478-7_30
Scopus: 2-s2.0-84990040923

9. Yandex, A.B., Lempitsky, V.
(2016).Efficient Indexing of Billion-Scale Datasets of Deep Descriptors.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2016-2055-2063
DOI: 10.1109/CVPR.2016.226
Scopus: 2-s2.0-84986247157

10. Docampo-Álvarez, B., Gómez-González, V., Montes-Campos, H., Otero-Mato, J.M., Méndez-Morales, T., Cabeza, O., Gallego, L.J., Lynden-Bell, R.M., Ivaništšev, V.B., Fedorov, M.V., Varela, L.M. (2016).Molecular dynamics simulation of the behaviour of water in nano-confined ionic liquid-water mixtures.
Journal of Physics Condensed Matter,28(46)
DOI: 10.1088/0953-8984/28/46/464001
Scopus: 2-s2.0-84989162335

11. Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.
(2016).Detecting overlapping instances in microscopy images using extremal region trees. Medical Image Analysis,273-16 DOI: 10.1016/j.media.2015.03.002 Scopus: 2-s2.0-84948090245

12. Ganin, Y., Kononenko, D., Sungatullina, D., Lempitsky, V.
(2016).DeepWarp: Photorealistic image resynthesis for gaze manipulation.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),9906311-326
DOI: 10.1007/978-3-319-46475-6_20
Scopus: 2-s2.0-84990855318

13. Burnaev, E., Smolyakov, D.
(2016).One-Class SVM with Privileged Information and Its Application to Malware Detection.
IEEE International Conference on Data Mining Workshops, ICDMW,0273-280
DOI: 10.1109/ICDMW.2016.0046
Scopus: 2-s2.0-85015178515

14. Rakhuba, M., Oseledets, I.
(2016).Calculating vibrational spectra of molecules using tensor train decomposition.
Journal of Chemical Physics,145(12)
DOI: 10.1063/1.4962420
Scopus: 2-s2.0-84988856982

15. Villena, J.F., Polimeridis, A.G., Eryaman, Y., Adalsteinsson, E., Wald, L.L., White, J.K., Daniel, L. (2016).Fast Electromagnetic Analysis of MRI Transmit RF Coils Based on Accelerated Integral Equation Methods.
IEEE Transactions on Biomedical Engineering,63(11) 2250-2261
DOI: 10.1109/TBME.2016.2521166
Scopus: 2-s2.0-84994047770

16. Misin, M., Palmer, D.S., Fedorov, M.V.
(2016).Predicting Solvation Free Energies Using Parameter-Free Solvent Models.
Journal of Physical Chemistry B,120(25) 5724-5731
DOI: 10.1021/acs.jpcb.6b05352
Scopus: 2-s2.0-84976871338

17. Badranova, G.U., Gotovtsev, P.M., Zubavichus, Y.V., Staroselskiy, I.A., Vasiliev, A.L., Trunkin, I.N., Fedorov, M.V.
(2016).Biopolymer-based hydrogels for encapsulation of photocatalytic TiO2 nanoparticles prepared by the freezing/thawing method.
Journal of Molecular Liquids,22316-20
DOI: 10.1016/j.molliq.2016.07.135
Scopus: 2-s2.0-84981507089

18. Jin, W., Polimeridis, A.G., Rodriguez, A.W.
(2016).Temperature control of thermal radiation from composite bodies.
Physical Review B,93(12) DOI: 10.1103/PhysRevB.93.121403
Scopus: 2-s2.0-84960945131

19. Li, X.H., Ortner, C., Shapeev, A.V., Van Koten, B.
(2016).Analysis of blended atomistic/continuum hybrid methods.
Numerische Mathematik,134(2) 275-326
DOI: 10.1007/s00211-015-0772-z
Scopus: 2-s2.0-84947597514

20. Rakhuba, M.V., Oseledets, I.V.
(2016).Grid-based electronic structure calculations: The tensor decomposition approach.
Journal of Computational Physics,31219-30
DOI: 10.1016/j.jcp.2016.02.023
Scopus: 2-s2.0-84958259400

21. Serralles, J.E.C., Daniel, L., White, J.K., Sodickson, D.K., Lattanzi, R., Polimeridis, A.G. (2016).Global maxwell tomography: A novel technique for electrical properties mapping based on MR measurements and volume integral equation formulations.
2016 IEEE Antennas and Propagation Society International Symposium, APSURSI 2016 - Proceedings,1395-1396
DOI: 10.1109/APS.2016.7696404
Scopus: 2-s2.0-84997287932

22. Kuleshov, A., Bernstein, A.
(2016).Incremental construction of low-dimensional data representations.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),989655-67
DOI: 10.1007/978-3-319-46182-3_5
Scopus: 2-s2.0-84988019865

23. Jin, W., Khandekar, C., Pick, A., Polimeridis, A.G., Rodriguez, A.W.
(2016).Amplified and directional spontaneous emission from arbitrary composite bodies: A self-consistent treatment of Purcell effect below threshold.
Physical Review B,93(12)
DOI: 10.1103/PhysRevB.93.125415
Scopus: 2-s2.0-84960884107

24. Olson, D., Shapeev, A.V., Bochev, P.B., Luskin, M.
(2016).Analysis of an optimization-based atomistic-to-continuum coupling method for point defects. ESAIM: Mathematical Modelling and Numerical Analysis,50(1) 1-41
DOI: 10.1051/m2an/2015023
Scopus: 2-s2.0-84947435482

25. Frolov, E., Oseledets, I.
(2016).Fifty shades of ratings: How to benefit from a negative feedback in top-N recommendations tasks.
RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems,91-98
DOI: 10.1145/2959100.2959170
Scopus: 2-s2.0-84991246626

26. Nazarenko, D.V., Kharyuk, P.V., Oseledets, I.V., Rodin, I.A., Shpigun, O.A.
(2016).Machine learning for LC-MS medicinal plants identification.
Chemometrics and Intelligent Laboratory Systems,156174-180
DOI: 10.1016/j.chemolab.2016.06.003
Scopus: 2-s2.0-84974733845

27. Bozyk, L., Chill, F., Litsarev, M.S., Tolstikhina, I.Y., Shevelko, V.P.
(2016).Multiple-electron losses in uranium ion beams in heavy ion synchrotrons.
Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms,372102-108
DOI: 10.1016/j.nimb.2016.01.047
Scopus: 2-s2.0-84961183501

28. Artemov, A., Burnaev, E.
(2016).Detecting Performance Degradation of Software-Intensive Systems in the Presence of Trends and Long-Range Dependence.
IEEE International Conference on Data Mining Workshops, ICDMW,029-36
DOI: 10.1109/ICDMW.2016.0013
Scopus: 2-s2.0-85015185129

29. Misin, M., Vainikka, P.A., Fedorov, M.V., Palmer, D.S.
(2016).Salting-out effects by pressure-corrected 3D-RISM.
Journal of Chemical Physics,145(19)
DOI: 10.1063/1.4966973
Scopus: 2-s2.0-84996843195

30. Litsarev, M.S., Oseledets, I.V.
(2016).A low-rank approach to the computation of path integrals.
Journal of Computational Physics,305557-574
DOI: 10.1016/j.jcp.2015.11.009
Scopus: 2-s2.0-84946781795

31. Sushnikova, D.A., Oseledets, I.V.
(2016).Preconditioners for hierarchical matrices based on their extended sparse form.
Russian Journal of Numerical Analysis and Mathematical Modelling,31(1) 29-40
DOI: 10.1515/rnam-2016-0003
Scopus: 2-s2.0-84959018923

32. Kuleshov, A., Bernstein, A.
(2016).Regression on High-Dimensional Inputs.
IEEE International Conference on Data Mining Workshops, ICDMW,0732-739
DOI: 10.1109/ICDMW.2016.0108
Scopus: 2-s2.0-85015200942

33. Abdulle, A., Jecker, O., Shapeev, A.
(2016).An optimization based coupling method for multiscale problems.
Multiscale Modeling and Simulation,14(4) 1377-1416
DOI: 10.1137/15M105389X
Scopus: 2-s2.0-85007030751

34. Gagarina, G.Y., Moiseev, N.A., Ryzhakova, A.V., Ryzhakov, G.V.
(2016).Estimation and forecast of regional competitiveness level.
Economy of Region,12(4) 1040-1049
DOI: 10.17059/2016-4-6
Scopus: 2-s2.0-85009830114

35. Grinchuk, O., Lebedev, V., Lempitsky, V.
(2016).Learnable visual markers.
Advances in Neural Information Processing Systems,4150-4158
Scopus: 2-s2.0-85019241450

36. Kuzmin, A., Zhang, X., Finche, J., Feigin, M., Anthony, B.W., Lempitsky, V.
(2016).Fast low-cost single element ultrasound reflectivity tomography using angular distribution analysis.
Proceedings - International Symposium on Biomedical Imaging,2016-1021-1024
DOI: 10.1109/ISBI.2016.7493439
Scopus: 2-s2.0-84978388410

37. Oseledets, I.V., Rakhuba, M.V., Chertkov, A.V.
(2016).Black-box solver for one dimensional multiscale modelling using the QTT format.
ECCOMAS Congress 2016 - Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering,47938-7947
DOI: 10.7712/100016.2387.10906
Scopus: 2-s2.0-84995538631
38. Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.
(2017).Quantum machine learning.
Nature,549(7671) 195-202
DOI: 10.1038/nature23474
Scopus: 2-s2.0-8503075236

39. Klokov, R., Lempitsky, V.
(2017) Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models. Proceedings of the IEEE International Conference on Computer Vision,2017-863-872
DOI: 10.1109/ICCV.2017.99
Scopus: 2-s2.0-85041901821

40. Ulyanov, D., Vedaldi, A., Lempitsky, V.
(2017).Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017,2017-4105-4113
DOI: 10.1109/CVPR.2017.437
Scopus: 2-s2.0-85041924129

41. Pumarola, A., Vakhitov, A., Agudo, A., Sanfeliu, A., Moreno-Noguer, F.
(2017).PL-SLAM: Real-time monocular visual SLAM with points and lines. Proceedings - IEEE International Conference on Robotics and Automation,4503-4508
DOI: 10.1109/ICRA.2017.7989522
Scopus: 2-s2.0-85027998904

42. Yarotsky, D.
(2017).Error bounds for approximations with deep ReLU networks.
Neural Networks,94103-114
DOI: 10.1016/j.neunet.2017.07.002
Scopus: 2-s2.0-85026205840

43. Lefkimmiatis, S.
(2017).Non-local color image denoising with convolutional neural networks.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017,2017-5882-5891
DOI: 10.1109/CVPR.2017.623
Scopus: 2-s2.0-85040669990

44. Ustinova, E., Ganin, Y., Lempitsky, V.
(2017).Multi-Region bilinear convolutional neural networks for person re-identification.
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017,
DOI: 10.1109/AVSS.2017.8078460
Scopus: 2-s2.0-85039898482

45. Podryabinkin, E.V., Shapeev, A.V.
(2017).Active learning of linearly parametrized interatomic potentials.
Computational Materials Science,140171-180
DOI: 10.1016/j.commatsci.2017.08.031
Scopus: 2-s2.0-85028916984

46. Korolev, S., Safiullin, A., Belyaev, M., Dodonova, Y.
(2017).Residual and plain convolutional neural networks for 3D brain MRI classification.
Proceedings - International Symposium on Biomedical Imaging,835-838
DOI: 10.1109/ISBI.2017.7950647
Scopus: 2-s2.0-85023166391

47. Cichocki, A., Phan, A.-H., Zhao, Q., Lee, N., Oseledets, I., Sugiyama, M., Mandic, D. (2017).Tensor networks for dimensionality reduction and large-scale optimizations: Part 2 applications and future perspectives.
Foundations and Trends in Machine Learning,9(6) 431-673
DOI: 10.1561/2200000067
Scopus: 2-s2.0-85013421573

48. Jin, J., Zhang, H., Daly, I., Wang, X., Cichocki, A.
(2017).An improved P300 pattern in BCI to catch user's attention.
Journal of Neural Engineering,14(3) DOI: 10.1088/1741-2552/aa6213
Scopus: 2-s2.0-85020445941

49. Qiu, Z., Allison, B.Z., Jin, J., Zhang, Y., Wang, X., Li, W., Cichocki, A.
(2017).Optimized motor imagery paradigm based on imagining Chinese characters writing movement.
IEEE Transactions on Neural Systems and Rehabilitation Engineering,25(7) 1009-1017
DOI: 10.1109/TNSRE.2017.2655542
Scopus: 2-s2.0-85021697996

50. Frolov, E., Oseledets, I. (2017).Tensor methods and recommender systems.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,7(3)
DOI: 10.1002/widm.1201
Scopus: 2-s2.0-85013874327

51. Guérin, B., Villena, J.F., Polimeridis, A.G., Adalsteinsson, E., Daniel, L., White, J.K., Wald, L.L. (2017).The ultimate signal-to-noise ratio in realistic body models.
Magnetic Resonance in Medicine,78(5) 1969-1980
DOI: 10.1002/mrm.26564
Scopus: 2-s2.0-85006412344

52. Che, M., Cichocki, A., Wei, Y.
(2017).Neural networks for computing best rank-one approximations of tensors and its applications.
Neurocomputing,267114-133
DOI: 10.1016/j.neucom.2017.04.058
Scopus: 2-s2.0-85020678476

53. Ermilov, D., Panov, M., Yanovich, Y.
(2017).Automatic bitcoin address clustering.
Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017,2017-461-466
DOI: 10.1109/ICMLA.2017.0-118
Scopus: 2-s2.0-85048493464

54. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.
(2017).Domain-adversarial training of neural networks.
Advances in Computer Vision and Pattern Recognition,(9783319583464) 189-209
DOI: 10.1007/978-3-319-58347-1_10
Scopus: 2-s2.0-85029580147

55. Burnaev, E., Panin, I., Sudret, B.
(2017).Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions. Annals of Mathematics and Artificial Intelligence,81(1-2) 187-207
DOI: 10.1007/s10472-017-9542-1
Scopus: 2-s2.0-85016626814

56. Matveev, S.A., Krapivsky, P.L., Smirnov, A.P., Tyrtyshnikov, E.E., Brilliantov, N.V. (2017).Oscillations in Aggregation-Shattering Processes.
Physical Review Letters,119(26)
DOI: 10.1103/PhysRevLett.119.260601
Scopus: 2-s2.0-85039753641

57. Coles, S.W., Mishin, M., Perkin, S., Fedorov, M.V., Ivaništšev, V.B.
(2017).The nanostructure of a lithium glyme solvate ionic liquid at electrified interfaces.
Physical Chemistry Chemical Physics,19(18) 11004-11010
DOI: 10.1039/c7cp00837f
Scopus: 2-s2.0-85024493739

58. Zaytsev, A., Burnaev, E.
(2017).Large scale variable fidelity surrogate modeling.
Annals of Mathematics and Artificial Intelligence,81(1-2) 167-186
DOI: 10.1007/s10472-017-9545-y
Scopus: 2-s2.0-85017166284

59. Evfratov, S.A., Osterman, I.A., Komarova, E.S., Pogorelskaya, A.M., Rubtsova, M.P., Zatsepin, T.S., Semashko, T.A., Kostryukova, E.S., Mironov, A.A., Burnaev, E., Krymova, E., Gelfand, M.S., Govorun, V.M., Bogdanov, A.A., Sergiev, P.V., Dontsova, O.A.
(2017).Application of sorting and next generation sequencing to study 5'-UTR influence on translation efficiency in Escherichia coli.
Nucleic Acids Research,45(6) 3487-3502
DOI: 10.1093/nar/gkw1141
Scopus: 2-s2.0-85018244910

60. Safin, A., Burnaev, E.
(2017).Conformal kernel expected similarity for anomaly detection in time-series data.
Advances in Systems Science and Applications,17(3) 22-33
Scopus: 2-s2.0-85040741526

61. Thiyam, D.B., Cruces, S., Olias, J., Cichocki, A.
(2017).Optimization of Alpha-Beta Log-Det divergences and their application in the spatial filtering of two class motor imagery movements. Entropy,19(3)
DOI: 10.3390/e19030089
Scopus: 2-s2.0-85014614134

62. Somov, A., Karelin, A., Baranov, A., Mironov, S.
(2017).Estimation of a Gas Mixture Explosion Risk by Measuring the Oxidation Heat Within a Catalytic Sensor.
IEEE Transactions on Industrial Electronics,64(12) 9691-9698
DOI: 10.1109/TIE.2017.2716882
Scopus: 2-s2.0-85021810729

63. Kuzmin, A., Mikushin, D., Lempitsky, V.
(2017).End-to-End learning of cost-volume aggregation for real-time dense stereo.
IEEE International Workshop on Machine Learning for Signal Processing, MLSP,2017-1-6
DOI: 10.1109/MLSP.2017.8168183
Scopus: 2-s2.0-85042268731

64. Li, J., Li, C., Cichocki, A.
(2017).Canonical Polyadic Decomposition with Auxiliary Information for Brain-Computer Interface. IEEE Journal of Biomedical and Health Informatics,21(1) 263-271
DOI: 10.1109/JBHI.2015.2491645
Scopus: 2-s2.0-85027555543

65. Egorova, E., Kabatiansky, G.
(2017).Analysis of two tracing traitor schemes via coding theory.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),1049584-92
DOI: 10.1007/978-3-319-66278-7_8
Scopus: 2-s2.0-85028543430

66. Tichavský, P., Phan, A.-H., Cichocki, A.
(2017).Non-orthogonal tensor diagonalization. Signal Processing,138313-320
DOI: 10.1016/j.sigpro.2017.04.001
Scopus: 2-s2.0-85017217771

67. Ostanin, I., Safonov, A., Oseledets, I.
(2017).Natural erosion of sandstone as shape optimisation. Scientific Reports,7(1) DOI: 10.1038/s41598-017-17777-1
Scopus: 2-s2.0-85044924062

68. Babenko, A., Lempitsky, V.
(2017).Product split trees. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017,2017-6316-6324
DOI: 10.1109/CVPR.2017.669
Scopus: 2-s2.0-85040458659

69. Kuleshov, A., Bernstein, A., Burnaev, E.
(2017).Mobile robot localization via machine learning.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),10358276-290 DOI: 10.1007/978-3-319-62416-7_20
Scopus: 2-s2.0-85025129532

70. Shapeev, A.
(2017).Accurate representation of formation energies of crystalline alloys with many components. Computational Materials Science,13926-30
DOI: 10.1016/j.commatsci.2017.07.010
Scopus: 2-s2.0-85026488809

71. Kruglik, S., Frolov, A.
(2017).Bounds and constructions of codes with all-symbol locality and availability.
IEEE International Symposium on Information Theory - Proceedings,1023-1027
DOI: 10.1109/ISIT.2017.8006683
Scopus: 2-s2.0-85034023353

72. Xie, K., He, Z., Cichocki, A., Fang, X.
(2017).Rate of Convergence of the FOCUSS Algorithm. IEEE Transactions on Neural Networks and Learning Systems,28(6) 1276-1289
DOI: 10.1109/TNNLS.2016.2532358
Scopus: 2-s2.0-84960155651

73. Saucedo, A., Lefkimmiatis, S., Rangwala, N., Sung, K.
(2017).Improved Computational Efficiency of Locally Low Rank MRI Reconstruction Using Iterative Random Patch Adjustments.
IEEE Transactions on Medical Imaging,36(6) 1209-1220
DOI: 10.1109/TMI.2017.2659742
Scopus: 2-s2.0-85021424381

74. Yandex, A.B., Lempitsky, V.
(2017).AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding.
Proceedings of the IEEE International Conference on Computer Vision,2017-4895-4903
DOI: 10.1109/ICCV.2017.523
Scopus: 2-s2.0-85041915658

75. Zagidullin, R.R., Smirnov, A.P., Matveev, S.A., Tyrtyshnikov, E.E.
(2017).An efficient numerical method for a mathematical model of a transport of coagulating particles. Moscow University Computational Mathematics and Cybernetics,41(4) 179-186
DOI: 10.3103/S0278641917040082
Scopus: 2-s2.0-85036612520

76. Yurchenko, V., Lempitsky, V.
(2017).Parsing images of overlapping organisms with deep singling-out networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017,2017-4752-4760
DOI: 10.1109/CVPR.2017.505
Scopus: 2-s2.0-85044300308

77. Kuleshov, A., Bernstein, A.
(2017).Nonlinear multi-output regression on unknown input manifold. Annals of Mathematics and Artificial Intelligence,81(1-2) 209-240
DOI: 10.1007/s10472-017-9551-0
Scopus: 2-s2.0-85019211409

78. Fonarev, A., Hrinchuk, O., Gusev, G., Serdyukov, P., Oseledets, I.
(2017).Riemannian optimization for skip-gram negative sampling. ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers),12028-2036
DOI: 10.18653/v1/P17-1185
Scopus: 2-s2.0-85040935996

79. Kuleshov, A., Bernstein, A., Burnaev, E., Yanovich, Y.
(2017).Machine learning in appearance-based robot self-localization.
Proceedings - 16th IEEE International Conference on Machine Learning and Applications,2017-106-112
DOI: 10.1109/ICMLA.2017.0-171
Scopus: 2-s2.0-85046494570

80. Burnaev, E.V., Golubev, G.K.
(2017).On One Problem in Multichannel Signal Detection. Problems of Information Transmission,53(4) 368-380
DOI: 10.1134/S0032946017040056
Scopus: 2-s2.0-85041549792

81. Deshpande, G., Rangaprakash, D., Oeding, L., Cichocki, A., Hu, X.P.
(2017).A new generation of brain-computer interfaces driven by discovery of latent EEG-fMRI linkages using tensor decomposition.
Frontiers in Neuroscience,11
DOI: 10.3389/fnins.2017.00246
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383. Kostyukevich, Y., Vladimirov, G., Stekolschikova, E., Ivanov, D., Yablokov, A., Zherebker, A., Sosnin, S., Orlov, A., Fedorov, M., Khaitovich, P., Nikolaev, E.
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387. Peng, Y., Tang, R., Kong, W., Zhang, J., Nie, F., Cichocki, A.
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390. Matveev, S.A., Stefonishin, D.A., Smirnov, A.P., Sorokin, A.A., Tyrtyshnikov, E.E. (2019).Numerical studies of solutions for kinetic equations with many-particle collisions. Journal of Physics: Conference Series,1163(1)
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392. Smolyakov, D., Sviridenko, N., Ishimtsev, V., Burikov, E., Burnaev, E.
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395. Malovichko, M., Tarasov, A.V., Yavich, N., Zhdanov, M.S.
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396. Davydov, V., Gazaryan, A., Madhwal, Y., Yanovich, Y.
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397. Ibrahimov, R., Tsykunov, E., Shirokun, V., Somov, A., Tsetserukou, D.
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409. Matveev, S.A., Smirnov, A.P., Tyrtyshnikov, E.E.
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412. Dzis, A., Rybin, P., Frolov, A.
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413. Pukalchik, M.A., Katrutsa, A.M., Shadrin, D., Terekhova, V.A., Oseledets, I.V.
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415. Velichkovsky, B.B., Khromov, N., Korotin, A., Burnaev, E., Somov, A.
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416. Ustinova, D., Rybin, P., Frolov, A.
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417. Seipt, D., Kharin, V.Y., Rykovanov, S.G.
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418. Pavlov, S.V., Nazmutdinov, R.R., Fedorov, M.V., Kislenko, S.A.
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419. Rybin, P., Frolov, A.
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425. Brilliantov, N.V.
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427. Smerdov, A., Burnaev, E., Somov, A.
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428. Korepanova, D., Nosyk, M., Ostrovsky, A., Yanovich, Y.
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430. Kabatiansky, G.
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431. Karlov, D.S., Popov, P., Sosnin, S., Fedorov, M.V.
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432. Usatyuk, V., Vorobyev, I.
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433. Popov, P., Grudinin, S., Kurdiuk, A., Buslaev, P., Redon, S.
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434. Guseynov, A.-A.D., Pisarev, S.A., Shulga, D.A., Palyulin, V.A., Fedorov, M.V., Karlov, D.S. (2019).Computational characterization of the glutamate receptor antagonist perampanel and its close analogs: density functional exploration of conformational space and molecular docking study.
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435. Egorova, E.
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436. Yavich, N., Malovichko, M., Zhdanov, M.
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437. Plekhanova, Y.V., Tarasov, S.E., Somov, A.S., Bol'shin, D.S., Vishnevskaya, M.V., Gotovtsev, P.M., Reshetilov, A.N.
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439. Pastushkova, L.K., Kashirina, D.N., Brzhozovskiy, A.G., Kononikhin, A.S., Tiys, E.S., Ivanisenko, V.A., Koloteva, M.I., Nikolaev, E.N., Larina, I.M.
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440. Karlov, D.S., Barygin, O.I., Dron, M.Y., Palyulin, V.A., Grigoriev, V.V., Fedorov, M.V.
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442. Rybin, P., Frolov, A.
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443. Larina, I.M., Percy, A.J., Yang, J., Borchers, C.H., Nosovsky, A.M., Grigoriev, A.I., Nikolaev, E.N.
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445. Rivera-Castro, R., Nazarov, I., Xiang, Y., Maksimov, I., Pletnev, A., Burnaev, E.
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446. Kruglik, S., Rybin, P., Frolov, A.
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447. Luzik, D.A., Rogacheva, O.N., Izmailov, S.A., Indeykina, M.I., Kononikhin, A.S., Skrynnikov, N.R.
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448. Antipova, K., Klyuchnikov, N., Zaytsev, A., Gurina, E., Romanenkova, E., Koroteev, D. (2019).Data-driven model for the drilling accidents prediction.
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517. Tsukanov, A., Ivonin, D., Gotman, I., Gutmanas, E.Y., Grachev, E., Pervikov, A., Lerner, M.
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518. Jones, F.M., Arteta, C., Zisserman, A., Lempitsky, V., Lintott, C.J., Hart, T.
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519. Akter, M.S., Islam, M.R., Iimura, Y., Sugano, H., Fukumori, K., Wang, D., Tanaka, T., Cichocki, A. (2020).Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG.
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520. Arsalidou, M., Vijayarajah, S., Sharaev, M.
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521. Santo, K.P., Vishnyakov, A.
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522. Burmistrov, V., Morisseau, C., D'yachenko, V., Karlov, D., Butov, G.M., Hammock, B.D. (2020).Imidazolidine-2,4,5- and pirimidine-2,4,6-triones – New primary pharmacophore for soluble epoxide hydrolase inhibitors with enhanced water solubility.
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523. Pastushkova, L.H., Rusanov, V.B., Orlov, O.I., Goncharova, A.G., Chernikova, A.G., Kashirina, D.N., Kussmaul, A.R., Brzhozovskiy, A.G., Kononikhin, A.S., Kireev, K.S., Nosovsky, A.M., Nikolaev, E.N., Larina, I.M.
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524. Wang, J., Wu, M., Wu, L., Xu, Y., Li, F., Wu, Y., Popov, P., Wang, L., Bai, F., Zhao, S., Liu, Z.-J., Hua, T.
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525. Vishnyakov, A., Weathers, T., Hosangadi, A., Chiew, Y.C.
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526. Li, S., Jin, J., Daly, I., Zuo, C., Wang, X., Cichocki, A.
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527. Logacheva, M.D., Schelkunov, M.I., Fesenko, A.N., Kasianov, A.S., Penin, A.A. (2020).Mitochondrial genome of fagopyrum esculentum and the genetic diversity of extranuclear genomes in buckwheat.
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528. Tokareva, A.O., Chagovets, V.V., Starodubtseva, N.L., Nazarova, N.M., Nekrasova, M.E., Kononikhin, A.S., Frankevich, V.E., Nikolaev, E.N., Sukhikh, G.T.
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529. Burgess, S., Wang, Z., Vishnyakov, A., Neimark, A.V.
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530. Volkhonskiy, D., Nazarov, I., Burnaev, E.
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531. Vishnyakov, A., Weathers, T., Hosangadi, A., Chiew, Y.C.
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532. Shadrin, D., Pukalchik, M., Kovaleva, E., Fedorov, M.
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533. Bychkova, A.V., Lopukhova, M.V., Wasserman, L.A., Pronkin, P.G., Degtyarev, Y.N., Shalupov, A.I., Vasilyeva, A.D., Yurina, L.V., Kovarski, A.L., Kononikhin, A.S., Nikolaev, E.N.
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534. Gubarev, V.M., Yakovlev, V.Y., Sertsu, M.G., Yakushev, O.F., Krivtsun, V.M., Gladush, Y.G., Ostanin, I.A., Sokolov, A., Schäfers, F., Medvedev, V.V., Nasibulin, A.G.
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535. Starodubtseva, N., Nizyaeva, N., Baev, O., Bugrova, A., Gapaeva, M., Muminova, K., Kononikhin, A., Frankevich, V., Nikolaev, E., Sukhikh, G.
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536. Bondarenko, A., Hagen, M., Potthast, M., Wachsmuth, H., Beloucif, M., Biemann, C., Panchenko, A., Stein, B.
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537. Vrubel, I.I., Pervishko, A.A., Herper, H., Brena, B., Eriksson, O., Yudin, D.
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538. Matveev, A., Artemov, A., Zorin, D., Burnaev, E.
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539. Shtratnikova, V.Y., Rudenskaya, Y.A., Gerasimov, E.S., Schelkunov, M.I., Logacheva, M.D., Kolesnikov, A.A.
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540. Uvarov, A., Biamonte, J.D., Yudin, D.
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541. Davydov, V., Yanovich, Y.
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542. Poverennaya, E., Kiseleva, O., Ilgisonis, E., Novikova, S., Kopylov, A., Ivanov, Y., Kononikhin, A., Gorshkov, M., Kushlinskii, N., Archakov, A., Ponomarenko, E.
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543. Fokina, D., Muravleva, E., Ovchinnikov, G., Oseledets, I.
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544. Shtratnikova, V.Y., Schelkunov, M.I., Penin, A.A., Logacheva, M.D.
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545. Egiazarian, V., Ignatyev, S., Artemov, A., Voynov, O., Kravchenko, A., Zheng, Y., Velho, L., Burnaev, E.
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546. Shadrin, D., Menshchikov, A., Somov, A., Bornemann, G., Hauslage, J., Fedorov, M. (2020).Enabling Precision Agriculture through Embedded Sensing with Artificial Intelligence.
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547. Egorova, E., Fernandez, M., Kabatiansky, G.
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548. Gaither, C., Popp, R., Mohammed, Y., Borchers, C.H.
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549. Beliavskaia, A.Y., Predeus, A.V., Garushyants, S.K., Logacheva, M.D., Gong, J., Zou, S., Gelfand, M.S., Rautian, M.S.
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550. Zherebker, A., Yakimov, B., Rubekina, A., Kharybin, O., Fedoros, E.I., Perminova, I.V., Shirshin, E., Nikolaev, E.N.
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551. Koshelev, I., Somov, A., Lefkimmiatis, S., Rodriguez-Sanchez, A.
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552 Malovichko, M., Khokhlov, N., Yavich, N., Zhdanov, M.S.
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553. Koposov, D., Semenova, M., Somov, A., Lange, A., Stepanov, A., Burnaev, E.
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554. Bakulin, I., Zabirova, A., Lagoda, D., Poydasheva, A., Cherkasova, A., Pavlov, N., Kopnin, P., Sinitsyn, D., Kremneva, E., Fedorov, M., Gnedovskaya, E., Suponeva, N., Piradov, M. (2020).Combining HF rTMS over the left DLPFC with concurrent cognitive activity for the offline modulation of working memory in healthy volunteers: A proof-of-concept study.
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555. Pukalchik, M., Kydralieva, K., Yakimenko, O., Terekhova, V.
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556. Kabatiansky, G., Egorova, E.
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557. Vrubel, I.I., Pervishko, A.A.
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558. Richard, V.R., Zahedi, R.P., Eintracht, S., Borchers, C.H.
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559. Crochemore, M., Héliou, A., Kucherov, G., Mouchard, L., Pissis, S.P., Ramusat, Y.
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560 Velichkovsky, B., Nedoluzhko, A., Goldberg, E., Efimova, O., Sharko, F., Rastorguev, S., Krasivskaya, A., Sharaev, M., Korosteleva, A., Ushakov, V.
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561. Artemenkov, A., Panov, M.
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562. Alanov, A., Kochurov, M., Volkhonskiy, D., Yashkov, D., Burnaev, E., Vetrov, D.
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563. Koshev, N., Yavich, N., Malovichko, M., Skidchenko, E., Fedorov, M.
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564. Barabanau, I., Artemov, A., Burnaev, E., Murashkin, V.
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565. Lehky, S.R., Phan, A.H., Cichocki, A., Tanaka, K.
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566. Holzbaur, L., Polyanskaya, R., Polyanskii, N., Vorobyev, I.
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567. Tichavsky, P., Phan, A.-H., Cichocki, A.
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568. Smirnov, A.S., Melnikova-Pitskhelauri, T.V., Sharaev, M.G., Zhukov, V.Y., Pogosbekyan, E.L., Afandiev, R.M., Bozhenko, A.A., Yarkin, V.E., Chekhonin, I.V., Buklina, S.B., Bykanov, A.E., Ogurtsova, A.A., Kulikov, A.S., Bernshtein, A.V., Burnaev, E.V., Pitskhelauri, D.I., Pronin, I.N. (2020).Resting-state fMRI in preoperative non-invasive mapping in patients with left hemisphere glioma.
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569. Temnyakova, N.S., Vasilenko, D.A., Barygin, O.I., Dron, M.Y., Averina, E.B., Grishin, Y.K., Grigoriev, V.V., Palyulin, V.A., Fedorov, M.V., Karlov, D.S.
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570. Belazzougui, D., Kucherov, G.
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571. Vasilyeva, A.D., Yurina, L.V., Leonova, V.B., Azarova, D.Y., Bugrova, A.E., Konstantinova, T.S., Indeykina, M.I., Kononikhin, A.S., Nikolaev, E.N., Rosenfeld, M.A.
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572. Andreev, K., Marshakov, E., Frolov, A.
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573. Phan, A.-H., Cichocki, A., Oseledets, I., Calvi, G.G., Ahmadi-Asl, S., Mandic, D.P.
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574. Eshghi, A., Pistawka, A.J., Liu, J., Chen, M., Sinclair, N.J.T., Hardie, D.B., Elliott, M., Chen, L., Newman, R., Mohammed, Y., Borchers, C.H.
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575. Stefonishin, D.A., Matveev, S.A., Zheltkov, D.A.
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576. Gavrilov, A.A., Zharikova, A.A., Galitsyna, A.A., Luzhin, A.V., Rubanova, N.M., Golov, A.K., Petrova, N.V., Logacheva, M.D., Kantidze, O.L., Ulianov, S.V., Magnitov, M.D., Mironov, A.A., Razin, S.V.
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577. Zherebker, A.Y., Rukhovich, G.D., Kharybin, O.N., Fedoros, E.I., Perminova, I.V., Nikolaev, E.N. (2020).Fourier transform ion cyclotron resonance mass spectrometry for the analysis of molecular composition and batch-to-batch consistency of plant-derived polyphenolic ligands developed for biomedical application.
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578. Polyanskii, N., Vorobyev, I.
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579. Kozlov, S., Poyda, A., Orlov, V., Malakhov, D., Ushakov, V., Sharaev, M.
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580. Muravleva, E.A., Oseledets, I.V., Koroteev, D.A.
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581. Vorobyev, I.
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582. Smirnov, A., Zaytsev, A.
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583. Nabieva, E., Sharma, S.M., Kapushev, Y., Garushyants, S.K., Fedotova, A.V., Moskalenko, V.N., Serebrenikova, T.E., Glazyrina, E., Kanivets, I.V., Pyankov, D.V., Neretina, T.V., Logacheva, M.D., Bazykin, G.A., Yarotsky, D.
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