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Plenary Reports

Corresponding Author and Article Title	Date of review	Status
M.I.Petrovskiy. DEEP LEARNING METHODS FOR THE TASKS OF CREATING “DIGITAL TWINS” FOR TECHNOLOGICA PROCESSES	07.09.2023	V.Ilyin Review Acccept

Track 1. Machine Learning in Fundamental Physics

Corresponding Author and Article Title	Date of review	Status
Ju.Dubenskaya et al., Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks	30/09/2023	Dudko Review Accept
R.R.Fitagdinov. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks	24/09/2023	Demichev Review Accept
K.A.Galaktionov / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data	21/09/2023	Dudko Review Accept
E.O.Gres. The selection of gamma events from IACT images with deep learning methods	30/09/2023	Dudko Review Accept
A.Kryukov. Preliminary results of convolutional neural network models in HiSCORE experiment	03/10/2023	Dolenko Review Answer Accept
A.Kryukov. The use of conditional variational autoencoders for simulation of EASs images from IACTs	21/09/2023	Krinitskiy Review Answer Accept
V.S.Latypova / Method for separating extensive air showers by primary mass using machine learning for a SPHERE-type Cherenkov telescope	27/08-01/09/2023	Demichev review Accept
A.Y.Leonov. Deep Learning for Angle of Arrival Prediction in the Baikal Neutrino Telescope	21/09/2023	Demichev Review Accept
A.V. Matseiko. Application of machine learning methods in Baikal-GVD:background noise rejection and selection of neutrinoinduced events		A.Kryukov Review Accept
A.D.Zaborenko. Novelty Detection Neural Networks for Model-Independent New Physics Search	21/09/2023	Ilyin Review Acccept

Track 2. Machine Learning in Natural Sciences

Corresponding Author and Article Title	Date of review	Status
M.Borisov. Estimating cloud base height from all-sky imagery using artificial neural networks	<27/09/2023	Streltsova Review Answer Consideration
S.Dolenko (A.Guskov). Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy	05/09/2023	V.Roudnev. Review Acccept
I.M.Gadzhiev. Classification Approach to Prediction of Geomagnetic Disturbances	<25/09/2023	Zrelov Review Accept
V.Golikov. Client-server application for automated estimation of the material composition of bottom sediments in the >0.1 mm fraction from microphotography using modern deep learning methods	21/09/2023	Ilyin Review Acccept
A.Kasatkin. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements		Demichev Review Accept
I.Khabutdinov. Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models	21/09/2023	Dolenko Review Answer Accept
M.Krinitsky. Estimating significant wave height from X-band navigation radar using convolutional neural networks		Dudko=Sep22 Review Answer Accept
M.A.Ledovskikh. Recognition of skin lesions from image	21/09/2023	Krinitsky Review Reject Demichev Review Accept Answer Consideration
A.V.Orekhov. Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve	13/09/2023	S.Dolenko. Review Accept
S.A.Pavlov. Application of Machine Learning Methods to Numerical Simulation of Hypersonic Flow	05/08/2023	A.Demichev. Review Answer Accept
V.Y.Rezvov. Improvement of the AI-based estimation of signifi cant wave height based on preliminary training on synthetic X-band radar sea clutter images	<25/09/2023	Streltsova Review Accept
O.E.Sarmanova. Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions	21/09/2023	Krinitsky Review Accept
A.Savin. SMAP sea surface salinity improvement in the Arctic region using machine learning approaches	13/09/2023	V.Roudnev. Review Answer Consideration
A.Tyshko. Automatic detection of acoustic signals from white whales and bottle-nosed dolphins	<25/09/2023	Zrelov Review Accept
A.V. Vorobev. Machine learning for diagnostics of space weather effects in the Arctic region	21/08/2023	E.Antonova Review Acccept-

Track 3. Modern Machine Learning Methods

Corresponding Author and Article Title	Date of review	Status
N.Y.Bykov / Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems	03/09/2023	M.Stepanova. Review Acccept
S.Dolenko. Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm	26/08-02/09/2023	M.Stepanova. Review Acccept
I.Isaev. The study of the integration of physical methods in the neural network solution of the inverse problem of exploration geophysics with variable physical properties of the medium	31/08-0/09/2023	V.Roudnev. Review Accept
Z.Kurdoshev. The importance of the number of overfits in time series forecasting by some optimizers and loss functions in neural networks	<25/09/2023	Dolenko Review Reject Demichev Review2 Reject Fin: Reject
D.N.Polyakov / Hyper-parameter tuning of neural network for high-dimensional problems in the case of Helmholtz equation	21/09/2023	Krinitsky Review Accept

Withdrawn papers

Corresponding Author and Article Title	Date of review	Status
A.Boukhanovsky. Generative AI for large models and digital twins
A.Kryukov. Machine Learning in Gamma Astronomy
L.Dudko. Methodology for the use of neural networks in the data analysis of the collider experiments
A.Hvatov. Robust equation discovery as a machine learning method	21/08/2023	Withdrawn
V.Kalnitsky. Modification of soft connectives in machine learning models	24/08/2023	Withdrawn
A.Polyakov. A technique for the total ozone columns retrieval using spectral measurements of the IKFS-2 instrument	21/08/2023	Withdrawn
A.Shevchenko. Determination of the charge of molecular fragments by machine learning methods	22/08/2023	Withdrawn
A.Vasiliev. The role of artificial intelligence in the preparation of modern scientific and pedagogical staff. The experience of the course “Neural networks and their application in scientific research” of Moscow State University named after M. V. Lomonosov
M.Zotov.Search for Meteors in the Mini-EUSO Orbital Telescope Data with Neural Networks