Table of Contents
Proceedings
Jan. 18, 2024
Вышел номер Вестника МГУ с трудами конференции: https://link.springer.com/journal/11972/volumes-and-issues/78-1/supplement
The proceedings of the DLCP2023 conference will be published as a special issue of the journal Moscow University Physics Bulletin in 2023 in both electronic and paper form. The journal is published in English by Springer and indexed in the databases WoS and Scopus and is included in the Russian index RCSI too.
Paper submission deadline is August 14, 2023 → Aug. 21, 2023 → Aug. 24, 2023 
After blind peer review, all accepted papers will be published in the conference proceedings.
Notification of paper acceptance — September 11, 2023 → September 25, 2023 → September 29, 2023→ October 05, 2023
The language of publications is English
All articles must additionally contain an abstract in Russian in PDF format.
We invite three types of submissions:
- INVITED PAPERS are of a review nature, which may contain significant original results (13-18 pages).
- REGULAR PAPERS describe research not published or submitted elsewhere (10-12 pages).
- SHORT PAPERS may be position papers, descriptions of research prospects, challenges, projects, ongoing works, or applications (5-9 pages).
Important. The title of the article should be the same as in the conference program. The first author is the speaker.
Prepared articles should be sent to the program committee by e-mail: dlcp2023@spbu.ru. By submitting an article, you agree to the rules of Moscow University Physics Bulletin. In particular, you must sign the COPYRIGHT TRANSFER AGREEMENT, which must be included in the package of submitted documents.
More details can be found at journal website.
Current status
Nov.20, 2023
- Авторам разосланы гранки. Окончательная версия должна поступить в редакцию не позднее 4 декабря 2023г.
- В конце ноября будут разосланы верстки для окончательной правки.
- DOI статей должны быть известны в начале декабря.
- Желающие могут получить письмо из издательства о принятии статьи в печать. Для этого надо написать мне запрос по электроной почте kryukov@theory.sinp.msu.ru.
- Тексты статей на сайте издательства будут доступны в январе 2024г.
TeX template
Jul 29, 2023
All papers must be prepared in LaTeX by using the Template. Example is here. Before the title, the authors should indicate the name of the section in accordance with the Book of Abstracts.
At the link https://www.overleaf.com/read/hjpdhfrhbjyd, the template for articles is available on the Overleaf in read-only mode. You need to create a copy of the project in Overleaf in order to work.
Please send to dlcp2023@spbu.ru a single zip-archive (dlcp2023-<last name of speaker>-<version>.zip) containing:
- the article in PDF format (dlcp2023-<last name of speaker>.pdf); the LaTeX file should be named dlcp2023-<last name of speaker>.tex;
- all the necessary files (tex, bibliography, pictures, etc.) to compile the pdf version into LaTeX;
- the signed scan of COPYRIGHT TRANSFER AGREEMENT (cta-<last name of speaker>.pdf) (the signing author warrants that he signs this Agreement as authorized agent for and on behalf of all the authors);
- abstract in Russian in PDF format (dlcp2023-<last name of speaker>-ru.pdf);
абстракт должен включать полный заголовок статьи (навание, список авторов и т.д.) на русском языке, повторяющий английскую версию.
The article must have a name in the same format: dlcp2023-<last name of speaker>.pdf.
Typical article formatting errors
- По возможности используйте UFT-8 кодировку.
- Не используйте русский алфавит в названиях файлов.
- Аннотация на русском языке должна быть отдельным файлом. Она должна включать название, авторов и их аффилиация, ключевые слова.
- В статье необходимо указать PACS номер. Его следует разместить перед Keywords. Список номеров можно скачать здесь. Если PACS номер для вашей темы отсутствует, то внесите “N/A” (Not applicable).
- Ссылки на литературу должны нумероваться в порядке их появления в тексте. В списке литературы инициалы авторов должны идти перед фамилией. Год издания следует после страниц: “том, стр1-стр2 (год)”
- Заголовки таблиц должны предшествовать самой таблице.
- Нумероваться должны только те формулы, на которые есть ссылки из текста.
- На все таблицы или рисунки должны быть ссылки из текста. Если ссылка отсутствует, то возможно такие таблицы и рисунки следует исключить.
- Обязательно наличие скана подписанного COPYRIGHT TRANSFER AGREEMENT (англ.).
Внимание! При повторной отсылке документов архив должен содержать полный список документов!
Более подробно правила изложены в образце.
Final version of the Proceedings
List of accepted papers
Отправляя статью в сборник трудов авторы дают согласие на ее открытую публикацию в журнале и несут полную ответственность за ее содержание.
Plenary Reports
| M.I.Petrovskiy. DEEP LEARNING METHODS FOR THE TASKS OF CREATING “DIGITAL TWINS” FOR TECHNOLOGICAL PROCESSES |
Track 1. Machine Learning in Fundamental Physics
| Ju.Dubenskaya. Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks | |
| R.R.Fitagdinov. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks | |
| K.A.Galaktionov / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data | |
| E.O.Gres. The selection of gamma events from IACT images with deep learning methods | |
| A.Kryukov. Preliminary results of convolutional neural network models in HiSCORE experiment | |
| A.Kryukov. The use of conditional variational autoencoders for simulation of EASs images from IACTs | |
| V.S.Latypova / Method for separating extensive air showers by primary mass using machine learning for a SPHERE-type Cherenkov telescope | |
| A.Y.Leonov. Deep Learning for Angle of Arrival Prediction in the Baikal Neutrino Telescope | |
| A.V. Matseiko. Application of machine learning methods in Baikal-GVD:background noise rejection and selection of neutrino induced events | |
| A.D.Zaborenko. Novelty Detection Neural Networks for Model-Independent New Physics Search |
Track 2. Machine Learning in Natural Sciences
| M.Borisov. Estimating cloud base height from all-sky imagery using artificial neural networks | |
| S.Dolenko(А.Guskov) Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy | |
| I.M.Gadzhiev. Classification Approach to Prediction of Geomagnetic Disturbances | |
| 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 | |
| A.Kasatkin. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements | |
| I.Khabutdinov. Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models | |
| M.Krinitsky. Estimating significant wave height from X-band navigation radar using convolutional neural networks | |
| M.A.Ledovskikh. Recognition of skin lesions from image | |
| A.V.Orekhov. Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve | |
| S.A.Pavlov. Application of Machine Learning Methods to Numerical Simulation of Hypersonic Flow | |
| 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 | |
| O.E.Sarmanova. Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions | |
| A.Savin. SMAP sea surface salinity improvement in the Arctic region using machine learning approaches | |
| A.Tyshko. Automatic detection of acoustic signals from white whales and bottle-nosed dolphins | |
| A.V. Vorobev. Machine learning for diagnostics of space weather effects in the Arctic region |
Track 3. Modern Machine Learning Methods
| N.Y.Bykov / Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems | |
| S.Dolenko. Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm | |
| 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 | |
| D.N.Polyakov / Hyper-parameter tuning of neural network for high-dimensional problems in the case of Helmholtz equation |