We give an alternative proof of a fact that a finite continuous non-decreasing submodular set function on a measurable space can be expressed as a supremum of measures dominated by the function, if there exists a class of sets which is totally ordered with respect to inclusion and generates the sigma-algebra of the space. The proof is elementary in the sense that the measure attaining the supremum in the claim is constructed by a standard extension theorem of measures. As a consequence, a uniquness of the supremum attaining measure also follows. A Polish space is an examples of the measurable space which has a class of totally ordered sets that generates the Borel sigma-algebra.
{"title":"An elementary proof of representation of submodular function as an supremum of measures on $σ$-algebra with totally ordered generating class","authors":"Tetsuya Hattori","doi":"arxiv-2406.18174","DOIUrl":"https://doi.org/arxiv-2406.18174","url":null,"abstract":"We give an alternative proof of a fact that a finite continuous\u0000non-decreasing submodular set function on a measurable space can be expressed\u0000as a supremum of measures dominated by the function, if there exists a class of\u0000sets which is totally ordered with respect to inclusion and generates the\u0000sigma-algebra of the space. The proof is elementary in the sense that the\u0000measure attaining the supremum in the claim is constructed by a standard\u0000extension theorem of measures. As a consequence, a uniquness of the supremum\u0000attaining measure also follows. A Polish space is an examples of the measurable\u0000space which has a class of totally ordered sets that generates the Borel\u0000sigma-algebra.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508369","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}
This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented.
{"title":"Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients","authors":"Parisa Davar, Frédéric Godin, Jose Garrido","doi":"arxiv-2406.15612","DOIUrl":"https://doi.org/arxiv-2406.15612","url":null,"abstract":"This paper tackles the problem of mitigating catastrophic risk (which is risk\u0000with very low frequency but very high severity) in the context of a sequential\u0000decision making process. This problem is particularly challenging due to the\u0000scarcity of observations in the far tail of the distribution of cumulative\u0000costs (negative rewards). A policy gradient algorithm is developed, that we\u0000call POTPG. It is based on approximations of the tail risk derived from extreme\u0000value theory. Numerical experiments highlight the out-performance of our method\u0000over common benchmarks, relying on the empirical distribution. An application\u0000to financial risk management, more precisely to the dynamic hedging of a\u0000financial option, is presented.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508370","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}
In this paper, we discuss aspects of model risk management in financial institutions which could be adopted by academic institutions to improve the process of conducting academic research, identify and mitigate existing limitations, decrease the possibility of erroneous results, and prevent fraudulent activities.
{"title":"Lessons From Model Risk Management in Financial Institutions for Academic Research","authors":"Mahmood Alaghmandan, Olga Streltchenko","doi":"arxiv-2406.14776","DOIUrl":"https://doi.org/arxiv-2406.14776","url":null,"abstract":"In this paper, we discuss aspects of model risk management in financial\u0000institutions which could be adopted by academic institutions to improve the\u0000process of conducting academic research, identify and mitigate existing\u0000limitations, decrease the possibility of erroneous results, and prevent\u0000fraudulent activities.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528296","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}
We study the general properties of robust convex risk measures as worst-case values under uncertainty on random variables. We establish general concrete results regarding convex conjugates and sub-differentials. We refine some results for closed forms of worstcase law invariant convex risk measures under two concrete cases of uncertainty sets for random variables: based on the first two moments and Wasserstein balls.
{"title":"Robust convex risk measures","authors":"Marcelo Righi","doi":"arxiv-2406.12999","DOIUrl":"https://doi.org/arxiv-2406.12999","url":null,"abstract":"We study the general properties of robust convex risk measures as worst-case\u0000values under uncertainty on random variables. We establish general concrete\u0000results regarding convex conjugates and sub-differentials. We refine some\u0000results for closed forms of worstcase law invariant convex risk measures under\u0000two concrete cases of uncertainty sets for random variables: based on the first\u0000two moments and Wasserstein balls.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"188 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508372","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}
Liyang Wang, Yu Cheng, Ao Xiang, Jingyu Zhang, Haowei Yang
This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP and its theoretical foundation, including text mining methods, NLP model design principles, and machine learning algorithms, are introduced. Second, the process of text data preprocessing and feature extraction is described. Finally, the effectiveness and predictive performance of the model are validated through empirical research. The results show that the NLP-based financial risk detection model performs excellently in risk identification and prediction, providing effective risk management tools for financial institutions. This study offers valuable references for the field of financial risk management, utilizing advanced NLP techniques to improve the accuracy and efficiency of financial risk detection.
{"title":"Application of Natural Language Processing in Financial Risk Detection","authors":"Liyang Wang, Yu Cheng, Ao Xiang, Jingyu Zhang, Haowei Yang","doi":"arxiv-2406.09765","DOIUrl":"https://doi.org/arxiv-2406.09765","url":null,"abstract":"This paper explores the application of Natural Language Processing (NLP) in\u0000financial risk detection. By constructing an NLP-based financial risk detection\u0000model, this study aims to identify and predict potential risks in financial\u0000documents and communications. First, the fundamental concepts of NLP and its\u0000theoretical foundation, including text mining methods, NLP model design\u0000principles, and machine learning algorithms, are introduced. Second, the\u0000process of text data preprocessing and feature extraction is described.\u0000Finally, the effectiveness and predictive performance of the model are\u0000validated through empirical research. The results show that the NLP-based\u0000financial risk detection model performs excellently in risk identification and\u0000prediction, providing effective risk management tools for financial\u0000institutions. This study offers valuable references for the field of financial\u0000risk management, utilizing advanced NLP techniques to improve the accuracy and\u0000efficiency of financial risk detection.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508371","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}
Decentralized Exchanges are becoming even more predominant in today's finance. Driven by the need to study this phenomenon from an academic perspective, the SIAG/FME Code Quest 2023 was announced. Specifically, participating teams were asked to implement, in Python, the basic functions of an Automated Market Maker and a liquidity provision strategy in an Automated Market Maker to minimize the Conditional Value at Risk, a critical measure of investment risk. As the competition's winning team, we highlight our approach in this work. In particular, as the dependence of the final return on the initial wealth distribution is highly non-linear, we cannot use standard ad-hoc approaches. Additionally, classical minimization techniques would require a significant computational load due to the cost of the target function. For these reasons, we propose a three-step approach. In the first step, the target function is approximated by a Kernel Ridge Regression. Then, the approximating function is minimized. In the final step, the previously discovered minimum is utilized as the starting point for directly optimizing the desired target function. By using this procedure, we can both reduce the computational complexity and increase the accuracy of the solution. Finally, the overall computational load is further reduced thanks to an algorithmic trick concerning the returns simulation and the usage of Cython.
{"title":"A Multi-step Approach for Minimizing Risk in Decentralized Exchanges","authors":"Daniele Maria Di Nosse, Federico Gatta","doi":"arxiv-2406.07200","DOIUrl":"https://doi.org/arxiv-2406.07200","url":null,"abstract":"Decentralized Exchanges are becoming even more predominant in today's\u0000finance. Driven by the need to study this phenomenon from an academic\u0000perspective, the SIAG/FME Code Quest 2023 was announced. Specifically,\u0000participating teams were asked to implement, in Python, the basic functions of\u0000an Automated Market Maker and a liquidity provision strategy in an Automated\u0000Market Maker to minimize the Conditional Value at Risk, a critical measure of\u0000investment risk. As the competition's winning team, we highlight our approach\u0000in this work. In particular, as the dependence of the final return on the\u0000initial wealth distribution is highly non-linear, we cannot use standard ad-hoc\u0000approaches. Additionally, classical minimization techniques would require a\u0000significant computational load due to the cost of the target function. For\u0000these reasons, we propose a three-step approach. In the first step, the target\u0000function is approximated by a Kernel Ridge Regression. Then, the approximating\u0000function is minimized. In the final step, the previously discovered minimum is\u0000utilized as the starting point for directly optimizing the desired target\u0000function. By using this procedure, we can both reduce the computational\u0000complexity and increase the accuracy of the solution. Finally, the overall\u0000computational load is further reduced thanks to an algorithmic trick concerning\u0000the returns simulation and the usage of Cython.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508373","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}
Alessandra Amendola, Vincenzo Candila, Antonio Naimoli, Giuseppe Storti
In order to meet the increasingly stringent global standards of banking management and regulation, several methods have been proposed in the literature for forecasting tail risk measures such as the Value-at-Risk (VaR) and Expected Shortfall (ES). However, regardless of the approach used, there are several sources of uncertainty, including model specifications, data-related issues and the estimation procedure, which can significantly affect the accuracy of VaR and ES measures. Aiming to mitigate the influence of these sources of uncertainty and improve the predictive performance of individual models, we propose novel forecast combination strategies based on the Model Confidence Set (MCS). In particular, consistent joint VaR and ES loss functions within the MCS framework are used to adaptively combine forecasts generated by a wide range of parametric, semi-parametric, and non-parametric models. Our results reveal that the proposed combined predictors provide a suitable alternative for forecasting risk measures, passing the usual backtests, entering the set of superior models of the MCS, and usually exhibiting lower standard deviations than other model specifications.
为了满足日益严格的全球银行管理和监管标准，文献中提出了几种预测尾部风险的方法，如风险价值（VaR）和预期跌幅（ES）。然而，无论采用哪种方法，都存在多种不确定性来源，包括模型规格、数据相关问题和估算程序，这些都会严重影响 VaR 和 ES 度量的准确性。为了减轻这些不确定性来源的影响并提高单个模型的预测性能，我们提出了基于模型置信集（MCS）的新型预测组合策略。特别是，MCS 框架内的一致联合 VaR 和 ES 损失函数被用来自适应地组合由各种参数、半参数和非参数模型生成的预测。我们的研究结果表明，所提出的组合预测器为预测风险度量提供了一个合适的替代方案，通过了通常的回溯测试，进入了 MCS 的优越模型集，并且通常比其他模型规格表现出更低的标准偏差。
{"title":"Adaptive combinations of tail-risk forecasts","authors":"Alessandra Amendola, Vincenzo Candila, Antonio Naimoli, Giuseppe Storti","doi":"arxiv-2406.06235","DOIUrl":"https://doi.org/arxiv-2406.06235","url":null,"abstract":"In order to meet the increasingly stringent global standards of banking\u0000management and regulation, several methods have been proposed in the literature\u0000for forecasting tail risk measures such as the Value-at-Risk (VaR) and Expected\u0000Shortfall (ES). However, regardless of the approach used, there are several\u0000sources of uncertainty, including model specifications, data-related issues and\u0000the estimation procedure, which can significantly affect the accuracy of VaR\u0000and ES measures. Aiming to mitigate the influence of these sources of\u0000uncertainty and improve the predictive performance of individual models, we\u0000propose novel forecast combination strategies based on the Model Confidence Set\u0000(MCS). In particular, consistent joint VaR and ES loss functions within the MCS\u0000framework are used to adaptively combine forecasts generated by a wide range of\u0000parametric, semi-parametric, and non-parametric models. Our results reveal that\u0000the proposed combined predictors provide a suitable alternative for forecasting\u0000risk measures, passing the usual backtests, entering the set of superior models\u0000of the MCS, and usually exhibiting lower standard deviations than other model\u0000specifications.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528272","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}
Alexander BakumenkoClemson University, USA, Kateřina Hlaváčková-SchindlerUniversity of Vienna, Austria, Claudia PlantUniversity of Vienna, Austria, Nina C. HubigClemson University, USA
Detecting anomalies in general ledger data is of utmost importance to ensure trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms to identify irregular or potentially fraudulent journal entries, each characterized by a varying number of transactions. In machine learning, heterogeneity in feature dimensions adds significant complexity to data analysis. In this paper, we introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. To encode non-semantic categorical data from real-world financial records, we tested 3 pre-trained general purpose sentence-transformer models. For the downstream classification task, we implemented and evaluated 5 optimized ML models including Logistic Regression, Random Forest, Gradient Boosting Machines, Support Vector Machines, and Neural Networks. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines, in selected settings even by a large margin. The findings further underscore the effectiveness of LLMs in enhancing anomaly detection in financial journal entries, particularly by tackling feature sparsity. We discuss a promising perspective on using LLM embeddings for non-semantic data in the financial context and beyond.
{"title":"Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs","authors":"Alexander BakumenkoClemson University, USA, Kateřina Hlaváčková-SchindlerUniversity of Vienna, Austria, Claudia PlantUniversity of Vienna, Austria, Nina C. HubigClemson University, USA","doi":"arxiv-2406.03614","DOIUrl":"https://doi.org/arxiv-2406.03614","url":null,"abstract":"Detecting anomalies in general ledger data is of utmost importance to ensure\u0000trustworthiness of financial records. Financial audits increasingly rely on\u0000machine learning (ML) algorithms to identify irregular or potentially\u0000fraudulent journal entries, each characterized by a varying number of\u0000transactions. In machine learning, heterogeneity in feature dimensions adds\u0000significant complexity to data analysis. In this paper, we introduce a novel\u0000approach to anomaly detection in financial data using Large Language Models\u0000(LLMs) embeddings. To encode non-semantic categorical data from real-world\u0000financial records, we tested 3 pre-trained general purpose sentence-transformer\u0000models. For the downstream classification task, we implemented and evaluated 5\u0000optimized ML models including Logistic Regression, Random Forest, Gradient\u0000Boosting Machines, Support Vector Machines, and Neural Networks. Our\u0000experiments demonstrate that LLMs contribute valuable information to anomaly\u0000detection as our models outperform the baselines, in selected settings even by\u0000a large margin. The findings further underscore the effectiveness of LLMs in\u0000enhancing anomaly detection in financial journal entries, particularly by\u0000tackling feature sparsity. We discuss a promising perspective on using LLM\u0000embeddings for non-semantic data in the financial context and beyond.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548173","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}
Fernando Acebes, David Curto, Juan de Anton, Felix Villafanez
Risk management is a fundamental discipline in project management, which includes, among others, quantitative risk analysis. Throughout several years of teaching, we have observed difficulties in students performing Monte Carlo Simulation within the quantitative analysis of risks. This article aims to present MCSimulRisk as a teaching tool that allows students to perform Monte Carlo simulation and apply it to projects of any complexity simply and intuitively. This tool allows for incorporating any uncertainty identified in the project into the model.
{"title":"Analisis cuantitativo de riesgos utilizando \"MCSimulRisk\" como herramienta didactica","authors":"Fernando Acebes, David Curto, Juan de Anton, Felix Villafanez","doi":"arxiv-2405.20688","DOIUrl":"https://doi.org/arxiv-2405.20688","url":null,"abstract":"Risk management is a fundamental discipline in project management, which\u0000includes, among others, quantitative risk analysis. Throughout several years of\u0000teaching, we have observed difficulties in students performing Monte Carlo\u0000Simulation within the quantitative analysis of risks. This article aims to\u0000present MCSimulRisk as a teaching tool that allows students to perform Monte\u0000Carlo simulation and apply it to projects of any complexity simply and\u0000intuitively. This tool allows for incorporating any uncertainty identified in\u0000the project into the model.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141255012","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}
Fernando Acebes, Javier Pajares, Jose M Gonzalez-Varona, Adolfo Lopez-Paredes
Project managers need to manage risks throughout the project lifecycle and, thus, need to know how changes in activity durations influence project duration and risk. We propose a new indicator (the Activity Risk Index, ARI) that measures the contribution of each activity to the total project risk while it is underway. In particular, the indicator informs us about what activities contribute the most to the project's uncertainty so that project managers can pay closer attention to the performance of these activities. The main difference between our indicator and other activity sensitivity metrics in the literature (e.g. cruciality, criticality, significance, or schedule sensitivity indices) is that our indicator is based on the Schedule Risk Baseline concept instead of on cost or schedule baselines. The new metric not only provides information at the beginning of the project, but also while it is underway. Furthermore, the ARI is the only one to offer a normalized result: if we add its value for each activity, the total sum is 100%.
{"title":"Project Risk Management from the bottom-up: Activity Risk Index","authors":"Fernando Acebes, Javier Pajares, Jose M Gonzalez-Varona, Adolfo Lopez-Paredes","doi":"arxiv-2406.00078","DOIUrl":"https://doi.org/arxiv-2406.00078","url":null,"abstract":"Project managers need to manage risks throughout the project lifecycle and,\u0000thus, need to know how changes in activity durations influence project duration\u0000and risk. We propose a new indicator (the Activity Risk Index, ARI) that\u0000measures the contribution of each activity to the total project risk while it\u0000is underway. In particular, the indicator informs us about what activities\u0000contribute the most to the project's uncertainty so that project managers can\u0000pay closer attention to the performance of these activities. The main\u0000difference between our indicator and other activity sensitivity metrics in the\u0000literature (e.g. cruciality, criticality, significance, or schedule sensitivity\u0000indices) is that our indicator is based on the Schedule Risk Baseline concept\u0000instead of on cost or schedule baselines. The new metric not only provides\u0000information at the beginning of the project, but also while it is underway.\u0000Furthermore, the ARI is the only one to offer a normalized result: if we add\u0000its value for each activity, the total sum is 100%.","PeriodicalId":501128,"journal":{"name":"arXiv - QuantFin - Risk Management","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141254933","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：