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444 results on '"Yu, Shuyan"'

1. On the price volatility of steel futures and its influencing factors in China

Author

Chen, Tzuchia, Li, Wenjing, and Yu, Shuyan

Subjects
Accounting. Bookkeeping, HF5601-5689
Abstract

Steel futures have the function of price discovery and hedging. Steel related enterprises can judge the hedging strategy through the direction of steel futures price volatility, and reasonably avoid the risk brought by price volatility. Therefore, it is particularly important to study steel futures price volatility and its influencing factors. Because steel futures in China have characteristics of peak and rear tail aggregation, the paper constructs Model of GARCH (1,1) to make positive analysis of futures price volatility and its influencing factors of deformed steel bars and hot rolled coils, and the following conclusions have been drawn: (1) The volume and open interest of deformed steel bars have very significant explanatory ability to futures price volatility of deformed steel bars; (2) The volume and open interest of hot rolled coils also have very significant explanatory ability to futures price volatility of hot rolled coils; (3) The sustainable capacity of the price volatility of deformed steel bars and hot rolled coils is relatively small; (4) Iron ore price have no obvious explanatory ability to futures price volatility. Finally, some managerial implications and suggestions are derived from the analysis of the proposed model.

Published
2021
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4. Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications

Author

Kamp, Serafina, Fayazi, Morteza, Benameur-El, Zineb, Yu, Shuyan, and Dreslinski, Ronald

Subjects
Computer Science - Information Retrieval
Abstract

With the abundant amount of available online and offline text data, there arises a crucial need to extract the relation between phrases and summarize the main content of each document in a few words. For this purpose, there have been many studies recently in Open Information Extraction (OIE). OIE improves upon relation extraction techniques by analyzing relations across different domains and avoids requiring hand-labeling pre-specified relations in sentences. This paper surveys recent approaches of OIE and its applications on Knowledge Graph (KG), text summarization, and Question Answering (QA). Moreover, the paper describes OIE basis methods in relation extraction. It briefly discusses the main approaches and the pros and cons of each method. Finally, it gives an overview about challenges, open issues, and future work opportunities for OIE, relation extraction, and OIE applications., Comment: 15 pages, 9 figures

Published
2023

20. Learning 3D Mineral Prospectivity from 3D Geological Models Using Convolutional Neural Networks: Application to a Structure-controlled Hydrothermal Gold Deposit

Author

Deng, Hao, Zheng, Yang, Chen, Jin, Yu, Shuyan, Xiao, Keyan, and Mao, Xiancheng

Subjects
Physics - Geophysics, Computer Science - Computer Vision and Pattern Recognition
Abstract

The three-dimensional (3D) geological models are the typical and key data source in the 3D mineral prospecitivity modeling. Identifying prospectivity-informative predictor variables from the 3D geological models is a challenging and tedious task. Motivated by the ability of convolutional neural networks (CNNs) to learn the intrinsic features, in this paper, we present a novel method that leverages CNNs to learn 3D mineral prospectivity from the 3D geological models. By exploiting the learning ability of CNNs, the presented method allows for disentangling complex correlation to the mineralization and thus opens a door to circumvent the tedious work for designing the predictor variables. Specifically, to explore the unstructured 3D geological models with the CNNs whose input should be structured, we develop a 2D CNN framework in which the geometry of geological boundary is compiled and reorganized into multi-channel images and fed into the CNN. This ensures an effective and efficient training of CNNs while allowing the prospective model to approximate the ore-forming process. The presented method is applied to a typical structure-controlled hydrothermal deposit, the Dayingezhuang gold deposit, eastern China, in which the presented method was compared with the prospectivity modeling methods using hand-designed predictor variables. The results demonstrate the presented method capacitates a performance boost of the 3D prospectivity modeling and empowers us to decrease work-load and prospecting risk in prediction of deep-seated orebodies.

Published
2021
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21. Tablext: A Combined Neural Network And Heuristic Based Table Extractor

Author

Colter, Zach, Fayazi, Morteza, Benameur-El, Zineb, Kamp, Serafina, Yu, Shuyan, and Dreslinski, Ronald

Subjects
Computer Science - Information Retrieval
Abstract

A significant portion of the data available today is found within tables. Therefore, it is necessary to use automated table extraction to obtain thorough results when data-mining. Today's popular state-of-the-art methods for table extraction struggle to adequately extract tables with machine-readable text and structural data. To make matters worse, many tables do not have machine-readable data, such as tables saved as images, making most extraction methods completely ineffective. In order to address these issues, a novel, general format table extractor tool, Tablext, is proposed. This tool uses a combination of computer vision techniques and machine learning methods to efficiently and effectively identify and extract data from tables. Tablext begins by using a custom Convolutional Neural Network (CNN) to identify and separate all potential tables. The identification process is optimized by combining the custom CNN with the YOLO object detection network. Then, the high-level structure of each table is identified with computer vision methods. This high-level, structural meta-data is used by another CNN to identify exact cell locations. As a final step, Optical Characters Recognition (OCR) is performed on every individual cell to extract their content without needing machine-readable text. This multi-stage algorithm allows for the neural networks to focus on completing complex tasks, while letting image processing methods efficiently complete the simpler ones. This leads to the proposed approach to be general-purpose enough to handle a large batch of tables regardless of their internal encodings or their layout complexity. Additionally, it becomes accurate enough to outperform competing state-of-the-art table extractors on the ICDAR 2013 table dataset.

Published
2021

46. Data Processing and Development of Big Data System: A Survey

Author

Yu, Shuyan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Xingming, editor, Zhang, Xiaorui, editor, Xia, Zhihua, editor, and Bertino, Elisa, editor

Published
2021
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49. Cloning, subcellular localization and prokaryotic expression of CmDIVfrom Chrysanthemum morifoliumcv. ‘Hangju’

Author

Zhang, Hong, Zhang, Caihong, Yu, Shuyan, Liu, Minhua, Qu, Zifan, Guo, Qiaosheng, and Wang, Tao

Abstract

Flower morphology is an important trait in seed plants, and many scientific studies have focused on the morphogenesis of flowers. The DIVARICATA (DIV) protein plays a key role in flower morphogenesis. In this study, the cDNA sequence of CmDIVwas isolated and cloned from C. morifoliumcv. ‘Hangju’ (GenBank accession number OR924116). Subcellular localization analysis indicated that CmDIV may be located in the nucleus and cell membrane. A comparison of multiple sequence alignments and phylogenetic relationships indicated that CmDIV was similar to DIVs from other plants. The study also revealed the expression pattern of CmDIVin ray florets and disk florets of C. morifoliumcv. ‘Hangju’. The pET28a-CmDIV fusion expression vector was constructed, the CmDIV fusion protein was obtained via a prokaryotic expression system, and the size of the CmDIV fusion protein was approximately 40 kDa. These results provide basic data to further knowledge on the biological functions of the CmDIVgene.

Published
2025
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