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dlcp21:abstracts [21/06/2021 22:59] – admindlcp21:abstracts [22/06/2021 16:14] – [Performance of convolutional neural networks processing simulated IACT images in the TAIGA experiment] admin
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-**N.V. Abasov, Melentiev Energy Systems Institute SB RAS** \\  +**N.V. Abasov, Melentiev Energy Systems Institute SB RAS, Russia** \\  
-E.N. Osipchuk, Melentiev Energy Systems Institute SB RAS \\  +E.N. Osipchuk, Melentiev Energy Systems Institute SB RAS, Russia \\  
-V.M. Berdnikov, Melentiev Energy Systems Institute SB RAS+V.M. Berdnikov, Melentiev Energy Systems Institute SB RAS, Russia
  
 //Short presentation (15 min)// //Short presentation (15 min)//
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 ===== Legacy of Tunka-Rex software and data. ===== ===== Legacy of Tunka-Rex software and data. =====
  
-**Pavel Bezyazeekov, API ISU**+**Pavel Bezyazeekov, API ISU, Russia**
  
 Tunka-Rex is a digital antenna array for measuring the radio emission from air-showers, induced by high-energy cosmic rays. The array started operation in 2012 with 18 antennas and had significantly developed over the years, finishing measurements in 2019 with 63 antennas and upgraded data acquisition. Analysis and processing of the collected data is a complex procedure which contains a number of steps (monitoring of state of the array, low-level filtration, quality cuts, reconstruction of air-shower parameters etc.). We give an overview of software developed for these tasks and our experience gained during the work with Tunka-Rex data. The legacy of software and data is discussed in the frame of the FAIR (Findability – Accessibility – Interoperability – Reuse) concepts. Tunka-Rex is a digital antenna array for measuring the radio emission from air-showers, induced by high-energy cosmic rays. The array started operation in 2012 with 18 antennas and had significantly developed over the years, finishing measurements in 2019 with 63 antennas and upgraded data acquisition. Analysis and processing of the collected data is a complex procedure which contains a number of steps (monitoring of state of the array, low-level filtration, quality cuts, reconstruction of air-shower parameters etc.). We give an overview of software developed for these tasks and our experience gained during the work with Tunka-Rex data. The legacy of software and data is discussed in the frame of the FAIR (Findability – Accessibility – Interoperability – Reuse) concepts.
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 ===== Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels ===== ===== Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels =====
  
-**A.Demichev, SINP MSU** +**A.Demichev, SINP MSU, Russia** 
  
 //Short presentation (15 min)// //Short presentation (15 min)//
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-**Elizaveta Gres, ISU, Irkutsk** \\  +**Elizaveta Gres, ISU, Irkutsk, Russia** \\  
-Alexander Kryukov, SINP MSU, Moscow+Alexander Kryukov, SINP MSU, Moscow, Russia
  
 //Short presentation (15 min)// //Short presentation (15 min)//
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 ===== Identifying partial differential equations of land surface schemes in INM climate models with neural networks ===== ===== Identifying partial differential equations of land surface schemes in INM climate models with neural networks =====
  
-**Mikhail Krinitskiy, Shirshov Institute of Oceanology, RAS** \\  +**Mikhail Krinitskiy, Shirshov Institute of Oceanology, RAS, Russia** \\  
-Viktor Stepanenko, Research Computing Center, MSU \\  +Viktor Stepanenko, Research Computing Center, MSU, Russia \\  
-Ruslan Chernyshev, Research Computing Center, MSU+Ruslan Chernyshev, Research Computing Center, MSU, Russia
  
-//Short presentation (30 min)//+//Long presentation (30 min)//
  
 The core of a land surface scheme in climate models is a solver for a nonlinear PDE system describing thermal conductance and water diffusion in soil. This system includes thermal conductivity and water diffusivity coefficients that are functions of the solution of the system, i.e., water vapor content W and soil temperature T. For the climate models to accurately represent the Earth system's evolution, one needs to identify the equations meaning either approximating the coefficients or estimating their values empirically. Measuring the coefficients is a complicated in-lab experiment without a chance to cover the full range of environmental conditions. The fact that there are many soil types obstructs comprehensive studies as well. There are also known approximate parametric forms of the coefficients that lack accuracy and, in turn, need their identification w.r.t. their own parameters. In this work, we propose a data-driven approach for approximating the parameters of the PDE system, describing the evolution of soil characteristics. We formulate the coefficients as parametric functions, namely artificial neural networks with expressive power high enough to represent a wide range of nonlinear functions. In a routine supervised data-driven problem, one needs to present ground truth for a target value. In the case of a soil PDE system, one cannot afford measurements of the full range of coefficients' ground truth values. In contrast with this approach, we propose training the neural networks with the loss function computed as a discrepancy between the PDE system solution and the measured characteristics W and T. We also propose a scheme inherited from the backpropagation method for calculating the gradients of the loss function w.r.t. network parameters. In contrast with recently developed physics-informed neural networks (PINN) methods, our approach is not meant to approximate a PDE solution directly. Instead, we state an inverse problem and propose its solution using artificial neural networks and the method for its optimization. As a very first step, we assessed the capabilities of our approach in four scenarios: a nonlinear thermal diffusion equation, a nonlinear water vapor W diffusion equation, Richards equation, and the system of thermal conductance equation and Richards equation. We generated realistic initial conditions and simulated synthetic evolutions of W and T that we used as measurements in the networks` training procedure in all these scenarios. We exploited batch normalization, learning rate schedule, and scheduling of additive noise rate to improve the convergence. We also added a few regularization terms to the loss function that penalize negative output values, non-zero output values in the origin, and negative gradients of the network w.r.t. its input. The results of our study show that our approach provides an opportunity for reconstructing the PDE coefficients of different forms accurately without actual knowledge of their ground truth values. The core of a land surface scheme in climate models is a solver for a nonlinear PDE system describing thermal conductance and water diffusion in soil. This system includes thermal conductivity and water diffusivity coefficients that are functions of the solution of the system, i.e., water vapor content W and soil temperature T. For the climate models to accurately represent the Earth system's evolution, one needs to identify the equations meaning either approximating the coefficients or estimating their values empirically. Measuring the coefficients is a complicated in-lab experiment without a chance to cover the full range of environmental conditions. The fact that there are many soil types obstructs comprehensive studies as well. There are also known approximate parametric forms of the coefficients that lack accuracy and, in turn, need their identification w.r.t. their own parameters. In this work, we propose a data-driven approach for approximating the parameters of the PDE system, describing the evolution of soil characteristics. We formulate the coefficients as parametric functions, namely artificial neural networks with expressive power high enough to represent a wide range of nonlinear functions. In a routine supervised data-driven problem, one needs to present ground truth for a target value. In the case of a soil PDE system, one cannot afford measurements of the full range of coefficients' ground truth values. In contrast with this approach, we propose training the neural networks with the loss function computed as a discrepancy between the PDE system solution and the measured characteristics W and T. We also propose a scheme inherited from the backpropagation method for calculating the gradients of the loss function w.r.t. network parameters. In contrast with recently developed physics-informed neural networks (PINN) methods, our approach is not meant to approximate a PDE solution directly. Instead, we state an inverse problem and propose its solution using artificial neural networks and the method for its optimization. As a very first step, we assessed the capabilities of our approach in four scenarios: a nonlinear thermal diffusion equation, a nonlinear water vapor W diffusion equation, Richards equation, and the system of thermal conductance equation and Richards equation. We generated realistic initial conditions and simulated synthetic evolutions of W and T that we used as measurements in the networks` training procedure in all these scenarios. We exploited batch normalization, learning rate schedule, and scheduling of additive noise rate to improve the convergence. We also added a few regularization terms to the loss function that penalize negative output values, non-zero output values in the origin, and negative gradients of the network w.r.t. its input. The results of our study show that our approach provides an opportunity for reconstructing the PDE coefficients of different forms accurately without actual knowledge of their ground truth values.
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-Alexander Sboev, National Research Centre "Kurchatov Institute" \\  +Alexander Sboev, National Research Centre "Kurchatov Institute", Russia \\  
-Ivan Moloshnikov, National Research Centre "Kurchatov Institute" \\  +Ivan Moloshnikov, National Research Centre "Kurchatov Institute", Russia \\  
-**Alexander Naumov, National Research Centre "Kurchatov Institute"** \\  +**Alexander Naumov, National Research Centre "Kurchatov Institute", Russia** \\  
-Anastasia Levochkina, National Research Centre "Kurchatov Institute"+Anastasia Levochkina, National Research Centre "Kurchatov Institute", Russia
  
 //Short presentation (15 min)// //Short presentation (15 min)//
  
 The problem of forecasting the evolution of the Covid-19 pandemic is extremely relevant because of the need for planning hospital beds demand and containment policies. Given the limitation of the available temporal datasets of the Covid-19 evolution, the complexity of machine learning algorithms to be created for pandemic time series data must not be high, and their efficiency mainly depends on the correct selection of highly-meaningful features. We view as one such feature the number of tweets where Internet users report having Covid-19. The task to extract such tweets from the internet is complicated by the lack of a Russian tweet dataset for training the machine learning models for extracting this category of tweets. In this work we present a corpus of about 10000 tweets labelled with the following classes: the Ill class when the authors declare that they are ill; the Recovered class when the authors declare that they have been ill and recovered, and the Others class of all other cases. Using this corpus, a Data-Driven model based on XLM-RoBERTa language model has been trained. It demonstrates the F1-macro accuracy of 0.85 for binary task (class 1 – Ill / Recovered, class 2 – Others), and 0.60 for the five-class task (Ill with high/low confidence, Recovered with high/low confidence, Others). These results outperform the RuDR-BERT model by 5 to 7%. The XLM-RoBERTa model thus obtained has been applied to the binary classification task for the unlabeled data of 486 000 tweets. Based on the model’s predictions, a curve has been plotted of the COVID cases in Moscow from January 1, 2020 to March 1, 2021. This plot has been compared to the official statistic on the confirmed cases, and correlation analysis of these two curves, shifted from one another by 1 to 5 days, has been performed. This analysis shows the highest correlation to be when the true curve is shifted 4 days to the future relative to the predicted one. Therefore, the data from the corpus collected are 4 days ahead of the official statistic. Thus, the numbers of tweets collected have proven to be a helpful input feature to use within pandemic forecasting models. The problem of forecasting the evolution of the Covid-19 pandemic is extremely relevant because of the need for planning hospital beds demand and containment policies. Given the limitation of the available temporal datasets of the Covid-19 evolution, the complexity of machine learning algorithms to be created for pandemic time series data must not be high, and their efficiency mainly depends on the correct selection of highly-meaningful features. We view as one such feature the number of tweets where Internet users report having Covid-19. The task to extract such tweets from the internet is complicated by the lack of a Russian tweet dataset for training the machine learning models for extracting this category of tweets. In this work we present a corpus of about 10000 tweets labelled with the following classes: the Ill class when the authors declare that they are ill; the Recovered class when the authors declare that they have been ill and recovered, and the Others class of all other cases. Using this corpus, a Data-Driven model based on XLM-RoBERTa language model has been trained. It demonstrates the F1-macro accuracy of 0.85 for binary task (class 1 – Ill / Recovered, class 2 – Others), and 0.60 for the five-class task (Ill with high/low confidence, Recovered with high/low confidence, Others). These results outperform the RuDR-BERT model by 5 to 7%. The XLM-RoBERTa model thus obtained has been applied to the binary classification task for the unlabeled data of 486 000 tweets. Based on the model’s predictions, a curve has been plotted of the COVID cases in Moscow from January 1, 2020 to March 1, 2021. This plot has been compared to the official statistic on the confirmed cases, and correlation analysis of these two curves, shifted from one another by 1 to 5 days, has been performed. This analysis shows the highest correlation to be when the true curve is shifted 4 days to the future relative to the predicted one. Therefore, the data from the corpus collected are 4 days ahead of the official statistic. Thus, the numbers of tweets collected have proven to be a helpful input feature to use within pandemic forecasting models.
 +
 +===== Performance of convolutional neural networks processing simulated IACT images in the TAIGA experiment =====
 +
 +**Stanislav Polyakov**, SINP MSU, Russia \\ 
 +Alexander Kryukov,  SINP MSU, Russia \\ 
 +Evgeny Postnikov SINP MSU, Russia
 +
 +//Short presentation (15 min)//
 +
 +Extensive air showers created by high-energy particles interacting
 +with the Earth atmosphere can be detected using imaging atmospheric
 +Cherenkov telescopes (IACTs). The IACT images can be analyzed to
 +distinguish between the events caused by gamma rays and by hadrons and
 +to infer the parameters of the event such as the energy of the primary
 +particle. We use convolutional neural networks (CNNs) to analyze Monte
 +Carlo-simulated images of the telescopes of the TAIGA experiment. The
 +analysis includes selection of the images corresponding to the showers
 +caused by gamma rays and estimates of the energy of the gamma rays. We
 +compare performance of the CNNs using images from a single telescope
 +and the CNNs using images from two telescopes as inputs. \\ 
 +//Keywords: deep learning; convolutional neural networks; gamma astronomy;
 +extensive air shower; IACT; stereoscopic mode; TAIGA//
 ===== EVALUATION OF MACHINE LEARNING METHODS FOR RELATION EXTRACTION BETWEEN DRUG ADVERSE EFFECTS AND MEDICATIONS IN RUSSIAN TEXTS OF INTERNET USER REVIEWS ===== ===== EVALUATION OF MACHINE LEARNING METHODS FOR RELATION EXTRACTION BETWEEN DRUG ADVERSE EFFECTS AND MEDICATIONS IN RUSSIAN TEXTS OF INTERNET USER REVIEWS =====
  
-A.G. Sboev, NRC «Kurchatov Institute», NRNU MEPHI \\  +A.G. Sboev, NRC «Kurchatov Institute», NRNU MEPHI, Russia \\  
-**A.A. Selivanov, NRC «Kurchatov Institute»** \\  +**A.A. Selivanov, NRC «Kurchatov Institute», Russia** \\  
-R.B. Rybka, NRC «Kurchatov Institute»; I.A. Moloshnikov NRC «Kurchatov Institute»+R.B. Rybka, NRC «Kurchatov Institute», Russia \\  
 +I.A. Moloshnikov NRC «Kurchatov Institute», Russia
  
 //Short presentation (15 min)// //Short presentation (15 min)//
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 ===== Using modern machine learning methods on KASCADE data for science and education ===== ===== Using modern machine learning methods on KASCADE data for science and education =====
  
-**Victoria Tokareva, IAP KIT**+**Victoria Tokareva, IAP KIT, Germany**
  
 //Short presentation (15 min)// //Short presentation (15 min)//
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 ===== Gamma/hadron separation for a ground based IACT (imaging atmospheric Cherenkov telescope) in experiment TAIGA using machine learning methods Random Forest ===== ===== Gamma/hadron separation for a ground based IACT (imaging atmospheric Cherenkov telescope) in experiment TAIGA using machine learning methods Random Forest =====
  
-**Vasyutina M.R., Moscow State University. Physical Department** \\  +**Vasyutina M.R., Moscow State University. Physical Department, Russia** \\  
-Sveshnikova L.G., Moscow State University. Skobeltsyn Institute of Nuclear Research.+Sveshnikova L.G., SINP MSU, Russia.
  
 //Short presentation (15 min)// //Short presentation (15 min)//
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 ===== Using convolutional neural network for analysis of HiSCORE events ===== ===== Using convolutional neural network for analysis of HiSCORE events =====
  
-**Vlaskina Anna, Physics Department, MSU** \\  +**Vlaskina Anna, Physics Department, MSU, Russia** \\  
-A. Kryukov, SINP MSU+A. Kryukov, SINP MSU, Russia
  
 //Short presentation (15 min)// //Short presentation (15 min)//
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 ===== Application of deep learning technique to an analysis of hard scattering processes at colliders ===== ===== Application of deep learning technique to an analysis of hard scattering processes at colliders =====
  
 +**A.Zaborenko, Faculty of Physics, M.V.Lomonosov Moscow State University, Russia** \\ 
 Lev Dudko, SINP MSU,Russia \\  Lev Dudko, SINP MSU,Russia \\ 
 Petr Volkov, SINP MSU,Russia \\  Petr Volkov, SINP MSU,Russia \\ 
dlcp21/abstracts.txt · Last modified: 22/06/2021 19:54 by admin