Doklady Mathematics最新文献_第7页

No Two Users Are Alike: Generating Audiences with Neural Clustering for Temporal Point Processes 没有两个用户是相同的：用神经聚类生成时点过程受众

IF 0.5 4区数学

Doklady Mathematics Pub Date : 2024-03-25 DOI: 10.1134/S1064562423701661

V. Zhuzhel, V. Grabar, N. Kaploukhaya, R. Rivera-Castro, L. Mironova, A. Zaytsev, E. Burnaev

{"title":"No Two Users Are Alike: Generating Audiences with Neural Clustering for Temporal Point Processes","authors":"V. Zhuzhel, V. Grabar, N. Kaploukhaya, R. Rivera-Castro, L. Mironova, A. Zaytsev, E. Burnaev","doi":"10.1134/S1064562423701661","DOIUrl":"10.1134/S1064562423701661","url":null,"abstract":"Identifying the right user to target is a common problem for different Internet platforms. Although numerous systems address this task, they are heavily tailored for specific environments and settings. It is challenging for practitioners to apply these findings to their problems. The reason is that most systems are designed for settings with millions of highly active users and with personal information, as is the case in social networks or other services with high virality. There exists a gap in the literature for systems that are for medium-sized data and where the only data available are the event sequences of a user. It motivates us to present Look-A-Liker (LAL) as an unsupervised deep cluster system. It uses temporal point processes to identify similar users for targeting tasks. We use data from the leading Internet marketplace for the gastronomic sector for experiments. LAL generalizes beyond proprietary data. Using event sequences of users, it is possible to obtain state-of-the-art results compared to novel methods such as Transformer architectures and multimodal learning. Our approach produces the up to 20% ROC AUC score improvement on real-world datasets from 0.803 to 0.959. Although LAL focuses on hundreds of thousands of sequences, we show how it quickly expands to millions of user sequences. We provide a fully reproducible implementation with code and datasets in https://github.com/adasegroup/sequence_clusterers.","PeriodicalId":531,"journal":{"name":"Doklady Mathematics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Barcodes as Summary of Loss Function Topology 条形码作为损失函数拓扑结构的总结

IF 0.5 4区数学

Doklady Mathematics Pub Date : 2024-03-25 DOI: 10.1134/S1064562423701570

S. A. Barannikov, A. A. Korotin, D. A. Oganesyan, D. I. Emtsev, E. V. Burnaev

引用次数: 0

Min-Max Optimization over Slowly Time-Varying Graphs 慢速时变图上的最小-最大优化

IF 0.5 4区数学

Doklady Mathematics Pub Date : 2024-03-25 DOI: 10.1134/S1064562423701533

Nhat Trung Nguyen, A. Rogozin, D. Metelev, A. Gasnikov

引用次数: 0

Automated System for Analysis of OCT Retina Images Development and Testing 开发和测试 OCT 视网膜图像自动分析系统

IF 0.5 4区数学

Doklady Mathematics Pub Date : 2024-03-25 DOI: 10.1134/S1064562423701545

L. E. Aksenova, K. D. Aksenov, E. V. Kozina, V. V. Myasnikova

{"title":"Automated System for Analysis of OCT Retina Images Development and Testing","authors":"L. E. Aksenova, K. D. Aksenov, E. V. Kozina, V. V. Myasnikova","doi":"10.1134/S1064562423701545","DOIUrl":"10.1134/S1064562423701545","url":null,"abstract":"Neovascular age-related macular degeneration (n-AMD) is a form of AMD that is responsible for most cases of severe vision loss. Anti-VEGF therapy, which is the gold standard for the treatment of this pathology, is accompanied by OCT monitoring. However, this process is hampered by the lack of methods for accurately quantifying OCT images. The aim of this study is to develop and evaluate the accuracy of the automated calculation of the quantitative characteristics of PED, SRF, and IRF biomarkers. A neural network with U-NET architecture was trained on a manually annotated dataset that included 385 OCT images. The dice coefficient measured on the validation dataset was 0.9, 0.72, and 0.69 for PED, SRF, and IRF. The results of the quantitative calculation of these biomarkers did not statistically differ from the measurements of an ophthalmologist. Comparison of groups with respect to the anatomical outcome of therapy showed that PED height, extent, and square are different for groups with adherence and non-adherence PED; and PED height, PED square, and IRF square are different for groups with nonadherence and tear PED. Thus, the algorithm for the quantitative calculation of biomarkers provides more information for assessing the results of therapy, which can improve the outcomes of treatment in patients with n-AMD.","PeriodicalId":531,"journal":{"name":"Doklady Mathematics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Possibility of Recovering Message Segments Based on Side Information about Original Characters 根据原始字符的侧面信息恢复信息片段的可能性

IF 0.5 4区数学

Doklady Mathematics Pub Date : 2024-03-25 DOI: 10.1134/S106456242370151X

A. G. Malashina

{"title":"Possibility of Recovering Message Segments Based on Side Information about Original Characters","authors":"A. G. Malashina","doi":"10.1134/S106456242370151X","DOIUrl":"10.1134/S106456242370151X","url":null,"abstract":"To provide secure information exchange in communication channels, the correctness of the operation of the relevant information protection systems must be preliminary studied. The mathematical algorithms used in such systems are correct and can theoretically provide the correct statistical properties of the output stream compared to the input. However, at the stage of implementation (programming) of these protection algorithms or at the stages of assembling the final equipment (using hardware, making adjustments) and its operation in real conditions, it is possible to introduce distortions that violate the operation of certain elements of information security tools (for example, a random number generator). As a result, by the nature of the transmitted signal, it becomes possible to determine that the output stream for a number of characteristics is steadily different from the ideal encrypted stream, which in theory should have come from the equipment and appeared at the output of the communication channel. In this situation, it is necessary to understand how the introduction of certain distortions affects the degree of security of the system being created. For this purpose, the parameters of various message sources are described, which simulate receiving an output stream with distortions. At the same time, the degree of security of the corresponding communication channel is proposed to be determined by estimating the proportion of the input stream that can be restored from the output using side information resulting from the introduction of appropriate distortions in the operation of the system.","PeriodicalId":531,"journal":{"name":"Doklady Mathematics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ESGify: Automated Classification of Environmental, Social, and Corporate Governance Risks ESGify：环境、社会和公司治理风险的自动分类

IF 0.5 4区数学

Doklady Mathematics Pub Date : 2024-03-25 DOI: 10.1134/S1064562423701673

A. Kazakov, S. Denisova, I. Barsola, E. Kalugina, I. Molchanova, I. Egorov, A. Kosterina, E. Tereshchenko, L. Shutikhina, I. Doroshchenko, N. Sotiriadi, S. Budennyy

{"title":"ESGify: Automated Classification of Environmental, Social, and Corporate Governance Risks","authors":"A. Kazakov, S. Denisova, I. Barsola, E. Kalugina, I. Molchanova, I. Egorov, A. Kosterina, E. Tereshchenko, L. Shutikhina, I. Doroshchenko, N. Sotiriadi, S. Budennyy","doi":"10.1134/S1064562423701673","DOIUrl":"10.1134/S1064562423701673","url":null,"abstract":"The growing recognition of environmental, social, and governance (ESG) factors in financial decision-making has spurred the need for effective and comprehensive ESG risk assessment tools. In this study, we introduce an open-source Natural Language Processing (NLP) model, “ESGify”1,2, based on MPNet-base architecture and aimed to classify texts within the frames of ESG risks. We also present a hierarchical and detailed methodology for ESG risk classification, leveraging the expertise of ESG professionals and global best practices. Anchored by a manually annotated multilabel dataset of 2000 news articles and domain adaptation with texts of sustainability reports, ESGify is developed to automate ESG risk classification following the established methodology. We compare augmentation techniques based on back translation and Large Language Models (LLMs) to improve the model quality and achieve 0.5 F1-weighted model quality in the dataset with 47 classes. This result outperforms ChatGPT 3.5 with a simple prompt. The model weights and documentation is hosted on Github https://github.com/sb-ai-lab/ESGify under the Apache 2.0 license.","PeriodicalId":531,"journal":{"name":"Doklady Mathematics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of Neural Network Algorithms for Human Movement Prediction Based on LSTM and Transformers 基于 LSTM 和变压器的人体运动预测神经网络算法研究

IF 0.5 4区数学

Doklady Mathematics Pub Date : 2024-03-25 DOI: 10.1134/S1064562423701624

S. V. Zhiganov, Y. S. Ivanov, D. M. Grabar

引用次数: 0

On Barenblatt–Zeldovich Intermediate Asymptotics 论巴伦布拉特-塞尔多维奇中间渐近线

IF 0.5 4区数学

Doklady Mathematics Pub Date : 2024-03-14 DOI: 10.1134/S1064562423701351

V. A. Kostin, D. V. Kostin, A. V. Kostin

引用次数: 0

Upper Bound for the Competitive Facility Location Problem with Demand Uncertainty 具有需求不确定性的竞争性设施选址问题的上限