Proceedings of The 6th International Workshop on Deep Learning in Computational Physics

Deep learning approach to high dimensional problems of quantum mechanics 量子力学高维问题的深度学习方法

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0013

V. Roudnev, M. Stepanova

{"title":"Deep learning approach to high dimensional problems of quantum mechanics","authors":"V. Roudnev, M. Stepanova","doi":"10.22323/1.429.0013","DOIUrl":"https://doi.org/10.22323/1.429.0013","url":null,"abstract":"Traditional linear approximation of quantum mechanical wave functions are not practically appli-cable for systems with more than 3 degrees of freedom due to the “the curse of dimensionality”. Indeed,the number of parameters required to describe a wave function in high-dimensional space grows exponentially with the number of degrees of freedom. Inevitably, strong model assumptions should be used when studying such systems numerically. There are, however, estimates of the complexity of a function reproduced by a deep neural network (DNN) that demonstrate the same exponential growth with respect to the number of the network layers. The number of parameters for DNN grows only linearly with the number of layers. This gives us a hope that application of DNN as an approximant for a wave function in high-dimensional space might moderate the computational requirements for reproducing such systems and make 4- or higher-dimensional systems feasible for direct numerical modeling. We present a study of DNN approximation properties for a multi-dimensional quantum harmonic oscillator. We demonstrate that the computational resources required to reproduce the wave function depend on the dimensionality of the problem and the quantum numbers of the state. Increasing the number of hidden layers in a fully-connected feed-forward DNN we can reproduce some excited states of a multidimensional system with computational resources comparable to low-dimensional cases. Using the DNN as an approximant for a wave function paves a way to developing a new class of computational schemes for solving the Schroedinger equation for high-dimensional systems.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128631121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relation Extraction from Texts Containing Pharmacologically Significant Information on base of Multilingual Language Models 基于多语言模型的药理学重要信息文本关系提取

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0014

A. Selivanov, A. Gryaznov, R. Rybka, A. Sboev, S. Sboeva, Yuliya Klueva

{"title":"Relation Extraction from Texts Containing Pharmacologically Significant Information on base of Multilingual Language Models","authors":"A. Selivanov, A. Gryaznov, R. Rybka, A. Sboev, S. Sboeva, Yuliya Klueva","doi":"10.22323/1.429.0014","DOIUrl":"https://doi.org/10.22323/1.429.0014","url":null,"abstract":"In this paper we estimate the accuracy of the relation extraction from texts containing pharmacologically significant information on base of the expanded version of RDRS corpus, which contains texts of internet reviews on medications in Russian. The accuracy of relation extraction is estimated and compared for two multilingual language models: XLM-RoBERTa-large and XLM-RoBERTa-large-sag. Earlier research proved XLM-RoBERTa-large-sag to be the most efficient language model for the previous version of the RDRS dataset for relation extraction using a ground-truth named entities annotation. In the current work we use two-step relation extraction approach: automated named entity recognition and extraction of relations between predicted entities. The implemented approach has given an opportunity to estimate the accuracy of the proposed solution to the relation extraction problem, as well as to estimate the accuracy at each step of the analysis. As a result, it is shown, that multilingual XLM-RoBERTa-large-sag model achieves relation extraction macro-averaged f1-score equals to 86.4% on the ground-truth named entities, 60.1% on the predicted named entities on the new version of the RDRS corpus contained more than 3800 annotated texts. Consequently, implemented approach based on the","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129464356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Stochastic vs. BFGS Training in Neural Discrimination of RF-Modulation 随机与BFGS训练在射频调制神经识别中的应用

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0011

M. Dima, M. Dima, M. Mihailescu

引用次数: 0

Application of convolutional neural networks for data analysis in TAIGA-HiSCORE experiment 卷积神经网络在TAIGA-HiSCORE实验数据分析中的应用

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0006

A. Vlaskina, A. Kryukov

{"title":"Application of convolutional neural networks for data analysis in TAIGA-HiSCORE experiment","authors":"A. Vlaskina, A. Kryukov","doi":"10.22323/1.429.0006","DOIUrl":"https://doi.org/10.22323/1.429.0006","url":null,"abstract":"The TAIGA experimental complex is a hybrid observatory for high-energy gamma-ray astronomy in the range from 10 TeV to several EeV. The complex consists of such installations as TAIGA- IACT, TAIGA-HiSCORE and a number of others. The TAIGA-HiSCORE facility is a set of wide-angle synchronized stations that detect Cherenkov radiation scattered over a large area. TAIGA-HiSCORE data provides an opportunity to reconstruct shower characteristics, such as shower energy, direction of arrival, and axis coordinates. The main idea of the work is to apply convolutional neural networks to analyze HiSCORE events, considering them as images. The distribution of registration times and amplitudes of events recorded by HiSCORE stations is used as input data. The paper presents the results of using convolutional neural networks to determine the characteristics of air showers. It is shown that even a simple model of convolutional neural network provides the accuracy of recovering EAS parameters comparable to the traditional method. Preliminary results of air shower parameters reconstruction obtained in a real experiment and their comparison with the results of traditional analysis are presented.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132405116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neuromorphic improvement of the Weizsäecker formula Weizsäecker配方的神经形态改进

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0029

M. Dima

引用次数: 0

Short-length peptides contact map prediction using Convolution Neural Networks 基于卷积神经网络的短肽接触图谱预测

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0016

Artem Maminov

{"title":"Short-length peptides contact map prediction using Convolution Neural Networks","authors":"Artem Maminov","doi":"10.22323/1.429.0016","DOIUrl":"https://doi.org/10.22323/1.429.0016","url":null,"abstract":"In this article, it is considered an approach for predicting the contact matrix (contact map) for short-length peptides. Contact matrix is two-dimensional representation of the protein. It can be used for tertiary structure reconstruction or for starting approximation in energy minimization models. For this work, peptides with a chain length from 15 up to 30 were chosen to test the model and simplify the calculations. Convolutional neural networks (CNNs) were used as a prediction tool according to the fact that the feature space of each peptide is presented as a two-dimensional matrix. SCRATCH tool was used to generate the secondary structure, solvent accessibility, and profile matrix (PSSM) for each peptide. CNN was implemented in the Python programming language using the Keras library. To work with the common PDB-format, which presents the structure information of proteins, the BioPython module was used. As a result, training, validation and test samples were generated, the multilayer multi-output convolutional neural network was constructed, which was trained and validated. The experiments were conducted on a test sample to predict the contact matrix and compare it with native one. To assess the quality of prediction, conjunction matrices for the threshold of 8 and 12 (cid:164) 𝐴 were formed, the metrics F1-score, recall and precision were calculated. According to F1-score, we can observe, that even with small neural network we can acheve quite good results. At the final step FT-COMAR tool was used to reconstruct tertiary structure of the proteins from its contact matrix. The results shows, that for reconstructed structures from 12 threshhold contact matrix, RMSD metric is better. ***","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133388821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A spiking neural network with fixed synaptic weights based on logistic maps for a classification task 一种基于逻辑映射的固定突触权值的脉冲神经网络

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0010

A. Sboev, Dmitriy Kunitsyn, A. Serenko, R. Rybka

{"title":"A spiking neural network with fixed synaptic weights based on logistic maps for a classification task","authors":"A. Sboev, Dmitriy Kunitsyn, A. Serenko, R. Rybka","doi":"10.22323/1.429.0010","DOIUrl":"https://doi.org/10.22323/1.429.0010","url":null,"abstract":"Spiking neural networks are increasingly popular for machine learning applications, thanks to ongoing progress in the hardware implementation of spiking networks in low-energy-consuming neuromorphic hardware. Still, obtaining a spiking neural network model that solves a classification task with the same level of accuracy as a artificial neural network remains a challenge. Of especial relevance is the development of spiking neural network models trained on base of local synaptic plasticity rules that can be implemented either in digital neuromorphic chips or in memristive devices. However, existing spiking networks with local learning all have, to our knowledge, one-layer topology, and no multi-layer ones have been proposed so far. As an initial step towards resolving this problem, we study the possibility of using a non-trainable layer of spiking neurons as an encoder layer within a prospective multi-layer spiking neural network, implying that the prospective subsequent layers could be trained on base of local plasticity. We study a spiking neural network model with non-trainable synaptic weights preset on base of logistic maps, similarly to what was proposed recently in the literature for formal neural networks. We show that one layer of spiking neurons with such weights can transform input vectors preserving the information about the classes of the input vectors, so that this information can be extracted from the neuron’s output spiking rates by","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133850288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accuracy of COVID-19 evolution models for different forecast horizons 不同预测视界下COVID-19演变模型的准确性

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0025

A. Sboev, S. Zavertyaev, I. Moloshnikov, A. Naumov, R. Rybka

{"title":"Accuracy of COVID-19 evolution models for different forecast horizons","authors":"A. Sboev, S. Zavertyaev, I. Moloshnikov, A. Naumov, R. Rybka","doi":"10.22323/1.429.0025","DOIUrl":"https://doi.org/10.22323/1.429.0025","url":null,"abstract":"Currently, the statistics on COVID-19 for many regions are accumulated for the time span of over than two years, which facilitates the use of data-driven algorithms, such as neural networks, for prediction of the disease's further development. This article provides a comparative analysis of various forecasting models of COVID-19 dynamics. The forecasting is performed for the period from 07/20/2020 to 05/05/2022 using statistical data from the regions of the Russian Federation and the USA. The forecast target is defined as the sum of confirmed cases over the forecast horizon. Models based on the Exponential Smoothing (ES) method and deep learning methods based on Long Short-Term Memory (LSTM) units were considered. The training data set included the data from all regions available in the full data set. The MAPE metric was used for model comparison, the evaluation of the effectiveness of LSTM in the learning process was carried out using cross-validation on the mean squared error (MSE) metric. The comparisons were made with the models from various literature sources, as well as with the baseline model \"tomorrow as today\" (for which the sum of cases over the forecast horizon is supposed to be equal to the current case number multiplied by the forecast horizon length). It was shown that on small horizons (up to 28 days) the \"tomorrow as today” model and ES algorithms show better accuracy than LSTM. In turn, on longer horizons (28 days or more), the preference should be given to the more complex LSTM-based model. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115341392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NARX neural prediction of oscillationalinstability at the IBR-2M reactor IBR-2M反应堆振荡不稳定性的NARX神经预测

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0031

M. Dima, M. Dima, M. Mihailescu

引用次数: 0

Google Earth Engine and machine learning for Earth monitoring 谷歌地球引擎和地球监测的机器学习

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022) Pub Date : 2022-11-14 DOI: 10.22323/1.429.0021

A. Uzhinskiy

{"title":"Google Earth Engine and machine learning for Earth monitoring","authors":"A. Uzhinskiy","doi":"10.22323/1.429.0021","DOIUrl":"https://doi.org/10.22323/1.429.0021","url":null,"abstract":"Hyperspectral images are a unique source for obtaining many kinds of information about the Earth's surface. Modern platforms support users to perform complex analyses with a collection of images without the use of any specialized software. Google Earth Engine (GEE) is a planetary-scale platform for Earth science data & analysis. Atmospheric, radiometric and geometric corrections have been made on number of image collections at GEE. While working with row data, it is possible to use build-in GEE function to filter data and create composites to get cloud score threshold and the percentile. It is also possible to use custom algorithms for atmospheric corrections. There are over 200 satellite image collections and modeled datasets. Some collections have a spatial resolution of up to 10 meters. GEE has the JavaScript online editor to create and verify code and Python API for advanced applications. All that made GEE very convenient tool for different Earth monitoring projects. Over the last decades there has been considerable progress in developing a machine learning methodology for a variety of Earth Science applications involving trace gases, retrievals, aerosol products, land surface products, vegetation indices, fire and flood tracking, ocean applications, and many others. In this report, we will review basic GEE functions and practice, some examples of successful applications, and our experience in environmental monitoring.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126333047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)最新文献