dlcp2025:review
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| dlcp2025:review [04/09/2025 16:49] – [Section 3. Machine Learning in Natural Sciences] admin | dlcp2025:review [15/09/2025 21:07] (current) – [Отозваны] admin | ||
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| || 40. Filtering of false EAS maxima using neural network methods in the SPHERE-3 experiment \\ +E.L. Entina | 25.08.2025 Получена+ \\ 26.08.2025 Рецензирование || | || 40. Filtering of false EAS maxima using neural network methods in the SPHERE-3 experiment \\ +E.L. Entina | 25.08.2025 Получена+ \\ 26.08.2025 Рецензирование || | ||
| || 75. Neural network modeling of optical solitons described by the generalized nonlinear Schrödinger equation of the sixth order with high nonlinearity \\ +I.A. Moloshnikov | 23.08.2025 Получена \\ 26.08.2025 Исправление+ \\ 28.08.2025 Рецензирование || | || 75. Neural network modeling of optical solitons described by the generalized nonlinear Schrödinger equation of the sixth order with high nonlinearity \\ +I.A. Moloshnikov | 23.08.2025 Получена \\ 26.08.2025 Исправление+ \\ 28.08.2025 Рецензирование || | ||
| - | || 42. Возможность применения метода нормализующих потоков для извлечения редких гамма событий в эксперименте TAIGA \\ +A.Kryukov | 26.08.2025 Получена | + | || 42. Возможность применения метода нормализующих потоков для извлечения редких гамма событий в эксперименте TAIGA \\ +A.Kryukov | 26.08.2025 Получена |
| || 41. SBI in dynamic data analysis of a multi-channel imaging detector \\ R.E. Saraev | 28.08.2025 Получена \\ 28.08.2025 Исправление+ \\ 29.08.2025 Рецензирование || | || 41. SBI in dynamic data analysis of a multi-channel imaging detector \\ R.E. Saraev | 28.08.2025 Получена \\ 28.08.2025 Исправление+ \\ 29.08.2025 Рецензирование || | ||
| - | || 76. Simulation of trawl processes using SINN architectures \\ K.E. Belkova | + | || 76. Simulation of trawl processes using SINN architectures \\ K.E. Belkova |
| ||39. ML-Based Optimum Sub-system Size for the GPU Implementation of the Tridiagonal Partition Method \\ +M. Veneva | ||39. ML-Based Optimum Sub-system Size for the GPU Implementation of the Tridiagonal Partition Method \\ +M. Veneva | ||
| || 44. Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models with Enhanced Nishimori Temperature \\ +V.S. Usatyuk | 25.08.2025 Получена \\ 26.08.2025 Исправление+ \\ 28.08.2025 Рецензирование || | || 44. Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models with Enhanced Nishimori Temperature \\ +V.S. Usatyuk | 25.08.2025 Получена \\ 26.08.2025 Исправление+ \\ 28.08.2025 Рецензирование || | ||
| - | || 43. Analysis of the TAIGA-HiSCORE Data using the Latent Space of Autoencoders \\ +Yu. Dubenskaya | + | || 43. Analysis of the TAIGA-HiSCORE Data using the Latent Space of Autoencoders \\ +Yu. Dubenskaya |
| - | || 81. Data augmentation problem for imaging atmospheric Cherenkov telescopes in stereo mode: the TAIGA-IACT Example \\ +D. Zhurov | + | || 81. Data augmentation problem for imaging atmospheric Cherenkov telescopes in stereo mode: the TAIGA-IACT Example \\ +D. Zhurov |
| - | || 95. Гамма-астрономия ультравысоких энергий и проект TAIGA-100 | + | |
| - | 11 | + | 10 |
| ===== Section 2. Machine Learning for Environmental Sciences ===== | ===== Section 2. Machine Learning for Environmental Sciences ===== | ||
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| || 73. Modeling turbulent transport of passive scalars in the planetary boundary layer using large eddy simulation and machine learning \\ I.A.Gerasimov | 28.08.2025 Получена \\ 03.09.2025 Исправление2 \\ 04.09.2025 Рецензирование || | || 73. Modeling turbulent transport of passive scalars in the planetary boundary layer using large eddy simulation and machine learning \\ I.A.Gerasimov | 28.08.2025 Получена \\ 03.09.2025 Исправление2 \\ 04.09.2025 Рецензирование || | ||
| || 45. Application of Convolutional Neural Networks for Upper Ionosphere Remote Sensing Using All-Sky Camera Data. \\ A.V. Vorobev| 16.08.2025 Получена+ \\ 28.08.2025 Исправление+ \\ 18.08.2025 Рецензирование || | || 45. Application of Convolutional Neural Networks for Upper Ionosphere Remote Sensing Using All-Sky Camera Data. \\ A.V. Vorobev| 16.08.2025 Получена+ \\ 28.08.2025 Исправление+ \\ 18.08.2025 Рецензирование || | ||
| - | || 82. COMPARISON OF MACHINE LEARNING METHODS FOR ACCOUNTING LAGGED RELATIONSHIPS IN URBAN HEAT ISLAND MODELING \\ K.F. Nazmutdinov | + | || 82. COMPARISON OF MACHINE LEARNING METHODS FOR ACCOUNTING LAGGED RELATIONSHIPS IN URBAN HEAT ISLAND MODELING \\ K.F. Nazmutdinov |
| - | || 56. Detection of Irminger Rings in high resolution ocean hydrodynamic modeling data using artificial neural networks \\ M. Kalinin | + | || 56. Detection of Irminger Rings in high resolution ocean hydrodynamic modeling data using artificial neural networks \\ M. Kalinin |
| || 92. Intercomparison of Machine Learning and Ingredient-Based Approaches for Identifying Hail-Prone Weather Conditions over Russia \\ P.D. Blinov | || 92. Intercomparison of Machine Learning and Ingredient-Based Approaches for Identifying Hail-Prone Weather Conditions over Russia \\ P.D. Blinov | ||
| - | || 54. Foundation Models of Ocean and Atmosphere in 2025: Milestones and Perspectives \\ M.A. Krinitskiy | + | || 54. Foundation Models of Ocean and Atmosphere in 2025: Milestones and Perspectives \\ M.A. Krinitskiy |
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| || 62. The creation of reasonable robot control behavior in the form of executable code \\ M.S. Skorokhodov | || 62. The creation of reasonable robot control behavior in the form of executable code \\ M.S. Skorokhodov | ||
| || 65. Temporal difference modulated spiking actor learning \\ Y. Tihomirov | || 65. Temporal difference modulated spiking actor learning \\ Y. Tihomirov | ||
| - | || 70. Building a Neural Ordinary Differential Equation Using Methods for Solving Inverse Problems of Dynamics \\ S.G. Shorokhov | + | || 70. Building a Neural Ordinary Differential Equation Using Methods for Solving Inverse Problems of Dynamics \\ S.G. Shorokhov |
| || 87. Application of the Transfer Learning Method for Convolutional Neural Network to Improve the Quality of Solving the Inverse Problem of Photoluminescent Nanosensors \\ G.N. Chugreeva | 25.08.2025 Получена \\ 28.08.2025 Исправление4+ \\ 30.08.2025 Рецензирование || | || 87. Application of the Transfer Learning Method for Convolutional Neural Network to Improve the Quality of Solving the Inverse Problem of Photoluminescent Nanosensors \\ G.N. Chugreeva | 25.08.2025 Получена \\ 28.08.2025 Исправление4+ \\ 30.08.2025 Рецензирование || | ||
| || 86. Probabilistic Spiking Neural Network with Correlation-based Memristive Synaptic Update \\ D. Kunitsyn | || 86. Probabilistic Spiking Neural Network with Correlation-based Memristive Synaptic Update \\ D. Kunitsyn | ||
| || 85. Finding optimal carbon dots synthesis parameters for quantitative analysis of components in multi-component aqueous solutions using machine learning \\ A.A. Guskov | || 85. Finding optimal carbon dots synthesis parameters for quantitative analysis of components in multi-component aqueous solutions using machine learning \\ A.A. Guskov | ||
| - | || 91. Classifying Russian speech commands with a hardware-deployable spiking neural network transferred from an artificial neural network \\ A. Serenko | + | || 91. Classifying Russian speech commands with a hardware-deployable spiking neural network transferred from an artificial neural network \\ A. Serenko |
| || 64. Optimization of IRT-T research reactor fuel loading pattern by genetic algorithm \\ N.V. Smolnikov | 25.08.2025 Получена+ \\ 27.08.2025 Исправление+ \\ 28.08.2025 Рецензирование || | || 64. Optimization of IRT-T research reactor fuel loading pattern by genetic algorithm \\ N.V. Smolnikov | 25.08.2025 Получена+ \\ 27.08.2025 Исправление+ \\ 28.08.2025 Рецензирование || | ||
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| || 74. СОЗДАНИЕ ДИНАМИЧЕСКОГО КОГНОВИЗОРА – РАСПОЗНАВАНИЕ КОГНИТИВНЫХ СОСТОЯНИЙ С ПОМОЩЬЮ МЕТОДОВ ГЛУБОКОГО ОБУЧЕНИЯ \\ А.С.Макаров | 29.08.2025 Отозвана | || 74. СОЗДАНИЕ ДИНАМИЧЕСКОГО КОГНОВИЗОРА – РАСПОЗНАВАНИЕ КОГНИТИВНЫХ СОСТОЯНИЙ С ПОМОЩЬЮ МЕТОДОВ ГЛУБОКОГО ОБУЧЕНИЯ \\ А.С.Макаров | 29.08.2025 Отозвана | ||
| || 96. Machine learning for statistical downscaling of precipitation spatial distribution characteristics in the Moscow region \\ Yarinich Yulia Ivanovna | 22.08.2025 Отозвана|| | || 96. Machine learning for statistical downscaling of precipitation spatial distribution characteristics in the Moscow region \\ Yarinich Yulia Ivanovna | 22.08.2025 Отозвана|| | ||
| + | || 95. Гамма-астрономия ультравысоких энергий и проект TAIGA-100 \\ +L.Kuzmichev | ||
| - | 6 | + | 7 |
| ++++ | ++++ | ||
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