dlcp2026:review
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| dlcp2026:review [29/07/2026 06:57] – [Section 3. Machine Learning for Environmental Sciences] admin | dlcp2026:review [05/08/2026 17:57] (current) – [Section 3. Machine Learning for Environmental Sciences] admin | ||
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| + | Следите за объявлениями на сайте. В случае возникновения вопросов просьба обращаться в программый комитет по почте [[dlcp@sinp.msu.ru]]. | ||
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| ====== Статус трудов ====== | ====== Статус трудов ====== | ||
| + | // | ||
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| + | Просьба в название архива включать номер секции. Например: | ||
| + | < | ||
| + | 1_99-Ivanov.zip | ||
| + | </ | ||
| + | , где 1 - номер секции, | ||
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| /** | /** | ||
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| - | Следите за объявлениями на сайте. В случае возникновения вопросов просьба обращаться в программый комитет по почте [[dlcp@sinp.msu.ru]]. | ||
| ++++ Легенда (Legend) | | ++++ Легенда (Legend) | | ||
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| |< - 70% 40% >| | |< - 70% 40% >| | ||
| - | ^^ Статья | + | ^ Статья |
| - | |^Title | + | | 27. Application of machine learning for the analysis of four-jet final states in the CEPC experiment |
| - | X | + | 1/13 |
| ===== Section 2. Machine Learning in Natural Sciences ===== | ===== Section 2. Machine Learning in Natural Sciences ===== | ||
| |< - 70% 30% >| | |< - 70% 30% >| | ||
| - | ^^ Статья, | + | ^ Статья, |
| - | |^Title | + | | 34. Detection of Slowly Developing Anomalies in Engineering Systems Based on Matrix Profile Family Algorithms and Multivariate Time Series Representation Methods \\ Anastasiia Kalita| 31.07.2026 Получена \\ 03.08.26 Исправление |
| + | | 13. A Method for the Automated Processing of Scientific Publications | ||
| + | | 32. Application of Convolutional Neural Networks and Autoencoders for the Development of Carbon Nanosensors with Optimal Luminescent Properties \\ G. N. Chugreeva | 05.08.26 Получена | ||
| - | X | + | 3/19 |
| ===== Section 3. Machine Learning for Environmental Sciences ===== | ===== Section 3. Machine Learning for Environmental Sciences ===== | ||
| |< - 70% 30% >| | |< - 70% 30% >| | ||
| - | ^^ Статья | + | ^ Статья |
| - | |^Convolutional Neural Networks and Bayesian Classification for Risk Assessment of High-Latitude Critical Infrastructure \\ A.V. Vorobev | + | |2. Convolutional Neural Networks and Bayesian Classification for Risk Assessment of High-Latitude Critical Infrastructure \\ A.V. Vorobev |
| + | | 20. ML-based local models of direct and diffuse shortwave irradiance in cloudy skies \\ N.A. Petrov | 05.08.26 Получена \\ 05.08.26 Исправление | ||
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| + | 2/16 | ||
| ===== Отозваны ===== | ===== Отозваны ===== | ||
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| - | ++++ Список | | ||
| Работы отозваны авторами, | Работы отозваны авторами, | ||
| + | ++++ Список | | ||
| |< - 70% 40% >| | |< - 70% 40% >| | ||
| - | |^Title \\ Author | + | ^Title \\ Author |
| + | | 31. Реконструкция ориентации многоканального изображающего детектора с помощью нейросетевой оценки отношения | ||
| + | ++++ | ||
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| + | 1 | ||
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dlcp2026/review.1785308221.txt.gz · Last modified: by admin
