Pub Date : 2024-08-10DOI: 10.48550/arXiv.2402.16609
Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su
{"title":"Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization","authors":"Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su","doi":"10.48550/arXiv.2402.16609","DOIUrl":"https://doi.org/10.48550/arXiv.2402.16609","url":null,"abstract":"","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920194","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}
Pub Date : 2024-07-26DOI: 10.48550/arXiv.2403.02618
Chao Cui, Jiankang Zhao
This paper introduces a learning-based calibration method tailored for microelectromechanical system (MEMS) gyroscopes. The proposed method integrates two linear networks, linked by a parametric rectified linear unit (PReLU), and boasts a compacted architecture with only 25 parameters. This simplicity allows for efficient training on a graphics processing unit (GPU) before deployment on resource-constrained microcontroller units (MCUs). The loss function has been carefully devised to strengthen the neural model by eliminating reliance on open-source datasets, and facilitates the swift collection of training data solely via a tri-axial manual rotation table. Furthermore, the proposed method has undergone rigorous validation through public datasets and real-world scenarios, which not only maintains its ultra-lightweight attributes but also outperforms other existing solutions in terms of accuracy. Experimental results demonstrate the method's practicality and efficacy, indicating its suitability for applications requiring inertial measurement units (IMUs). And the open-source implementation of this method is accessible at: https://github.com/tsuibeyond/TinyGC-Net
{"title":"TinyGC-Net: An Extremely Tiny Network for Calibrating MEMS Gyroscopes","authors":"Chao Cui, Jiankang Zhao","doi":"10.48550/arXiv.2403.02618","DOIUrl":"https://doi.org/10.48550/arXiv.2403.02618","url":null,"abstract":"\u0000 This paper introduces a learning-based calibration method tailored for microelectromechanical system (MEMS) gyroscopes. The proposed method integrates two linear networks, linked by a parametric rectified linear unit (PReLU), and boasts a compacted architecture with only 25 parameters. This simplicity allows for efficient training on a graphics processing unit (GPU) before deployment on resource-constrained microcontroller units (MCUs). The loss function has been carefully devised to strengthen the neural model by eliminating reliance on open-source datasets, and facilitates the swift collection of training data solely via a tri-axial manual rotation table. Furthermore, the proposed method has undergone rigorous validation through public datasets and real-world scenarios, which not only maintains its ultra-lightweight attributes but also outperforms other existing solutions in terms of accuracy. Experimental results demonstrate the method's practicality and efficacy, indicating its suitability for applications requiring inertial measurement units (IMUs). And the open-source implementation of this method is accessible at: https://github.com/tsuibeyond/TinyGC-Net","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801276","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}
Pub Date : 2024-07-01DOI: 10.48550/arXiv.2403.12873
Joshua Edward Hammond, Ricardo A. Lara Orozco, Michael Baldea, B. Korgel
{"title":"Short-Term Solar Irradiance Forecasting Under Data Transmission Constraints","authors":"Joshua Edward Hammond, Ricardo A. Lara Orozco, Michael Baldea, B. Korgel","doi":"10.48550/arXiv.2403.12873","DOIUrl":"https://doi.org/10.48550/arXiv.2403.12873","url":null,"abstract":"","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839089","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}
Self-supervised monocular depth estimation methods have been increasingly given much attention due to the benefit of not requiring large, labelled datasets. Such self-supervised methods require high-quality salient features and consequently suffer from severe performance drop for indoor scenes, where low-textured regions dominant in the scenes are almost indiscrimi-native. To address the issue, we propose a self-supervised indoor monocular depth estimation framework called F 2 Depth. A self-supervised optical flow estimation network is introduced to supervise depth learning. To improve optical flow estimation performance in low-textured areas, only some patches of
自我监督的单目深度估算方法无需大量标记数据集，因此越来越受到人们的关注。这种自监督方法需要高质量的突出特征，因此在室内场景中性能严重下降，因为室内场景中主要的低纹理区域几乎是无差别的。为了解决这个问题，我们提出了一种自监督室内单目深度估计框架，称为 F 2 Depth。我们引入了一个自监督光流估计网络来监督深度学习。为了提高在低纹理区域的光流估计性能，我们只使用了一些光流的补丁。
{"title":"F2Depth: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis","authors":"Xiaotong Guo, Huijie Zhao, Shuwei Shao, Xudong Li, Baochang Zhang","doi":"10.48550/arXiv.2403.18443","DOIUrl":"https://doi.org/10.48550/arXiv.2403.18443","url":null,"abstract":"Self-supervised monocular depth estimation methods have been increasingly given much attention due to the benefit of not requiring large, labelled datasets. Such self-supervised methods require high-quality salient features and consequently suffer from severe performance drop for indoor scenes, where low-textured regions dominant in the scenes are almost indiscrimi-native. To address the issue, we propose a self-supervised indoor monocular depth estimation framework called F 2 Depth. A self-supervised optical flow estimation network is introduced to supervise depth learning. To improve optical flow estimation performance in low-textured areas, only some patches of","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849334","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}
Center-based clustering has attracted significant research interest from both theory and practice. In many practical applications, input data often contain background knowledge that can be used to improve clustering results. In this work, we build on widely adopted k-center clustering and model its input background knowledge as must-link (ML) and cannot-link (CL) constraint sets. However, most clustering problems including k-center are inherently NP-hard, while the more complex constrained variants are known to suffer severer approximation and computation barriers that significantly limit their applicability. By employing a suite of techniques including reverse dominating sets, linear programming (LP) integral polyhedron, and LP duality, we arrive at the first efficient approximation algorithm for constrained k-center with the best possible ratio of 2. We also construct competitive baseline algorithms and empirically evaluate our approximation algorithm against them on a variety of real datasets. The results validate our theoretical findings and demonstrate the great advantages of our algorithm in terms of clustering cost, clustering quality, and running time.
基于中心的聚类在理论和实践方面都引起了极大的研究兴趣。在许多实际应用中，输入数据往往包含可用于改善聚类结果的背景知识。在这项工作中，我们以广泛采用的 k 中心聚类为基础，将其输入背景知识建模为必须链接（ML）和不能链接（CL）约束集。然而，包括 k 中心在内的大多数聚类问题本质上都是 NP 难的，而众所周知，更复杂的约束变体会遭遇更严重的近似和计算障碍，从而大大限制了其适用性。通过采用反向支配集、线性规划（LP）积分多面体和 LP 对偶性等一系列技术，我们首次为受限 k 中心问题提出了高效的近似算法，其最佳比率为 2。我们还构建了具有竞争力的基准算法，并在各种真实数据集上对我们的近似算法进行了实证评估。结果验证了我们的理论发现，并证明了我们的算法在聚类成本、聚类质量和运行时间方面的巨大优势。
{"title":"Efficient Constrained k-Center Clustering with Background Knowledge","authors":"Longkun Guo, Chaoqi Jia, Kewen Liao, Zhigang Lu, Minhui Xue","doi":"10.48550/arXiv.2401.12533","DOIUrl":"https://doi.org/10.48550/arXiv.2401.12533","url":null,"abstract":"Center-based clustering has attracted significant research interest from both theory and practice. In many practical applications, input data often contain background knowledge that can be used to improve clustering results. In this work, we build on widely adopted k-center clustering and model its input background knowledge as must-link (ML) and cannot-link (CL) constraint sets. However, most clustering problems including k-center are inherently NP-hard, while the more complex constrained variants are known to suffer severer approximation and computation barriers that significantly limit their applicability. By employing a suite of techniques including reverse dominating sets, linear programming (LP) integral polyhedron, and LP duality, we arrive at the first efficient approximation algorithm for constrained k-center with the best possible ratio of 2. We also construct competitive baseline algorithms and empirically evaluate our approximation algorithm against them on a variety of real datasets. The results validate our theoretical findings and demonstrate the great advantages of our algorithm in terms of clustering cost, clustering quality, and running time.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385936","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}
Pub Date : 2024-03-12DOI: 10.1609/aaai.v38i3.28030
Shiqi Jiang, Ning Li, Chen Shi, Liping Guo, Changbo Wang, Chenhui Li
The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA), playing a significant role in children's education. This task presents unique challenges, such as limited available data and the requirement for evaluation metrics from multiple perspectives. However, previous approaches have relied on training large datasets and subsequently providing an aesthetics score to the image, which is not applicable to AACP. To solve this problem, we construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning. 1) We build a novel dataset composed of two parts: the first part contains more than 20k unlabeled images of children's paintings; the second part contains 1.2k images of children's paintings, and each image contains eight attributes labeled by multiple design experts. 2) We design a pipeline that includes a feature extraction module, perception modules and a disentangled evaluation module. 3) We conduct both qualitative and quantitative experiments to compare our model's performance with five other methods using the AACP dataset. Our experiments reveal that our method can accurately capture aesthetic features and achieve state-of-the-art performance.
{"title":"AACP: Aesthetics Assessment of Children's Paintings Based on Self-Supervised Learning","authors":"Shiqi Jiang, Ning Li, Chen Shi, Liping Guo, Changbo Wang, Chenhui Li","doi":"10.1609/aaai.v38i3.28030","DOIUrl":"https://doi.org/10.1609/aaai.v38i3.28030","url":null,"abstract":"The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA), playing a significant role in children's education. This task presents unique challenges, such as limited available data and the requirement for evaluation metrics from multiple perspectives. However, previous approaches have relied on training large datasets and subsequently providing an aesthetics score to the image, which is not applicable to AACP. To solve this problem, we construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning. 1) We build a novel dataset composed of two parts: the first part contains more than 20k unlabeled images of children's paintings; the second part contains 1.2k images of children's paintings, and each image contains eight attributes labeled by multiple design experts. 2) We design a pipeline that includes a feature extraction module, perception modules and a disentangled evaluation module. 3) We conduct both qualitative and quantitative experiments to compare our model's performance with five other methods using the AACP dataset. Our experiments reveal that our method can accurately capture aesthetic features and achieve state-of-the-art performance.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395686","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}
Pub Date : 2024-03-12DOI: 10.1007/978-981-97-0989-2_14
M. Zajac, U. Störl
{"title":"Hybrid Data Management Architecture for Present Quantum Computing","authors":"M. Zajac, U. Störl","doi":"10.1007/978-981-97-0989-2_14","DOIUrl":"https://doi.org/10.1007/978-981-97-0989-2_14","url":null,"abstract":"","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395231","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}
Hyunsung Cho, Yukang Yan, Kashyap Todi, Mark Parent, Missie Smith, Tanya R. Jonker, Hrvoje Benko, David Lindlbauer
Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces.
{"title":"MineXR: Mining Personalized Extended Reality Interfaces","authors":"Hyunsung Cho, Yukang Yan, Kashyap Todi, Mark Parent, Missie Smith, Tanya R. Jonker, Hrvoje Benko, David Lindlbauer","doi":"10.1145/3613904.3642394","DOIUrl":"https://doi.org/10.1145/3613904.3642394","url":null,"abstract":"Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395111","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 the context of developing nations like India, traditional business to business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, ecommerce enterprises frequently. Established in 2016 with a vision to revolutionize trade in India through technology, Udaan is the countrys largest business to business ecommerce platform. Udaan operates across diverse product categories, including lifestyle, electronics, home and employ telecallers to cultivate buyer relationships, streamline order placement procedures, and promote special promotions. The accurate anticipation of buyer order placement behavior emerges as a pivotal factor for attaining sustainable growth, heightening competitiveness, and optimizing the efficiency of these telecallers. To address this challenge, we have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, show casing its potential for transformative impact in the ecommerce industry.
{"title":"A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce","authors":"Tuhin Subhra De, Pranjal Singh, Alok Patel","doi":"10.1145/3647750.3647754","DOIUrl":"https://doi.org/10.1145/3647750.3647754","url":null,"abstract":"In the context of developing nations like India, traditional business to business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, ecommerce enterprises frequently. Established in 2016 with a vision to revolutionize trade in India through technology, Udaan is the countrys largest business to business ecommerce platform. Udaan operates across diverse product categories, including lifestyle, electronics, home and employ telecallers to cultivate buyer relationships, streamline order placement procedures, and promote special promotions. The accurate anticipation of buyer order placement behavior emerges as a pivotal factor for attaining sustainable growth, heightening competitiveness, and optimizing the efficiency of these telecallers. To address this challenge, we have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, show casing its potential for transformative impact in the ecommerce industry.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395233","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}
Oshando Johnson, Goran Piskachev, Ranjith Krishnamurthy, Eric Bodden
To detect security vulnerabilities, static analysis tools need to be configured with security-relevant methods. Current approaches can automatically identify such methods using binary relevance machine learning approaches. However, they ignore dependencies among security-relevant methods, over-generalize and perform poorly in practice. Additionally, users have to nevertheless manually configure static analysis tools using the detected methods. Based on feedback from users and our observations, the excessive manual steps can often be tedious, error-prone and counter-intuitive. In this paper, we present Dev-Assist, an IntelliJ IDEA plugin that detects security-relevant methods using a multi-label machine learning approach that considers dependencies among labels. The plugin can automatically generate configurations for static analysis tools, run the static analysis, and show the results in IntelliJ IDEA. Our experiments reveal that Dev-Assist's machine learning approach has a higher F1-Measure than related approaches. Moreover, the plugin reduces and simplifies the manual effort required when configuring and using static analysis tools.
要检测安全漏洞，静态分析工具需要配置与安全相关的方法。目前的方法可以使用二进制相关性机器学习方法自动识别此类方法。然而，这些方法忽略了安全相关方法之间的依赖关系，过度泛化，在实践中表现不佳。此外，用户还必须使用检测到的方法手动配置静态分析工具。根据用户的反馈和我们的观察，过多的手动步骤往往是乏味、容易出错和违背直觉的。在本文中，我们介绍了 Dev-Assist，它是一个 IntelliJ IDEA 插件，可使用多标签机器学习方法检测与安全相关的方法，并考虑标签之间的依赖关系。该插件可以自动生成静态分析工具的配置，运行静态分析，并在 IntelliJ IDEA 中显示结果。我们的实验表明，与相关方法相比，Dev-Assist 的机器学习方法具有更高的 F1-Measure。此外，该插件还减少并简化了配置和使用静态分析工具时所需的手动操作。
{"title":"Detecting Security-Relevant Methods using Multi-label Machine Learning","authors":"Oshando Johnson, Goran Piskachev, Ranjith Krishnamurthy, Eric Bodden","doi":"10.1145/3643796.3648464","DOIUrl":"https://doi.org/10.1145/3643796.3648464","url":null,"abstract":"To detect security vulnerabilities, static analysis tools need to be configured with security-relevant methods. Current approaches can automatically identify such methods using binary relevance machine learning approaches. However, they ignore dependencies among security-relevant methods, over-generalize and perform poorly in practice. Additionally, users have to nevertheless manually configure static analysis tools using the detected methods. Based on feedback from users and our observations, the excessive manual steps can often be tedious, error-prone and counter-intuitive. In this paper, we present Dev-Assist, an IntelliJ IDEA plugin that detects security-relevant methods using a multi-label machine learning approach that considers dependencies among labels. The plugin can automatically generate configurations for static analysis tools, run the static analysis, and show the results in IntelliJ IDEA. Our experiments reveal that Dev-Assist's machine learning approach has a higher F1-Measure than related approaches. Moreover, the plugin reduces and simplifies the manual effort required when configuring and using static analysis tools.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395393","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}
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