dlcp2023:restricted:review
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Table of Contents
Review status
Шаблон рецензии в формате DOC
Шаблон письма с просьбой провести рецензирование: Request for review
Легенда.
- _blank_ - Articles not yet checked
- Check OK - Check OK and ready for review
- Accept - Article was accepted
- Reject - The article was rejected
- Revision - Article under revision
- Approve - In approval process
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 | 21/09/2023 | Dolenko<Oct01 Review Consideration |
|
| A.Kryukov. The use of conditional variational autoencoders for simulation of EASs images from IACTs | 21/09/2023 | Review | |
| 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 Revision<Sep29 |
||
| 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 Revision<Oct.01 |
|
| 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 Revision2<01.10 |
|
| M.Krinitsky. Estimating significant wave height from X-band navigation radar using convolutional neural networks | Dudko=Sep22 Review 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 Revision |
|
| 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 | |||
dlcp2023/restricted/review.1696054677.txt.gz · Last modified: 30/09/2023 09:17 by admin