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Research on dynamic structure of the exchange rate volatility network among the Belt and Road countries based on spillover effect

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APPLIED ECONOMICS LETTERS 2022年第5期29卷 446-454页

作者： Geng, Xueqing Guo, Kun Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

The countries taking part in China's 'The Belt and Road' Initiative have achieved greatly increased trade volume and become more firmly interconnected since the initiative was launched in September 2013. This paper researches the exchange rate volatility features among 15 countries along the Belt and Road region with the network analysis based on spillover effect. A directed network is constructed to show not only the impact one currency having on and received from the other currencies, but also the net effect from one currency to another in the network. The importance of any currency in the network and the total interconnectedness of the whole network can also be obtained. Furthermore, a 200-day rolling window analysis is used to show the dynamic evolution trend of the network. The results show that after the launch of the Belt and Road Initiative, both the interconnectedness of the exchange rate volatility network and the importance of CNY increase. In addition, the importance of most currencies in the network has been quite volatile.

关键词： Exchange rate volatility network analysis VAR model The B& R initiative

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Stock index prediction using global market indices: A Granger causality-based graph representation learning method 10

Stock index prediction using global market indices: A Grange...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Wang, Yunong Wu, Jie Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Global stock markets are increasingly connected and may experience strongly correlated stock price fluctuations due to external shocks, cooperation, and competition. In this paper, we propose a Granger causality-based graph representation learning method to improve the accuracy of stock index prediction. In the first stage, a Granger causality test is performed using the historical data of the target stock market and other global stock markets to select the neighboring stock markets with Granger causality and get the relational weight of each neighboring stock market. Then, the temporal weight of trading days is obtained based on the constructed VAR model coefficients. Subsequently, the daily graph embedding of the target stock index is learned by graph convolution operation for the prediction of the next day's closing price. The method developed in this paper experimented on four well-known Chinese stock indices, and it outperformed the benchmark model used in this paper. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Financial markets

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CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第4期21卷 823-834页

作者： Wu, Yirui Kong, Qiran Zhang, Lilai Castiglione, Aniello Nappi, Michele Wan, Shaohua Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing 210098 Jiangsu Peoples R China Hohai Univ Coll Comp Informat Nanjing 610023 Jiangsu Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat Knowledge Engn Changchun 130117 Peoples R China Univ Salerno Dept Management & Innovat Syst Via Giovanni Paolo 2 132 I-84084 Fisciano SA Italy Univ Salerno Dept Comp Sci I-132 Fisciano SA Italy Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Hechi Univ Key Lab AI & Informat Proc Yizhou 546300 Peoples R China

Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high computing cost, which prevents their further widely usage in practical scenarios. Moreover, it has been proved that exploring and embedding context knowledge in deep network can significantly improve accuracy. To emphasize these tips, we present CDT-CAD, i.e., context-aware deformable transformers for end-to-end chest abnormality detection on X-Ray images. CDT-CAD first constructs an iterative context-aware feature extractor, which not only enlarges receptive fields to encode multi-scale context information via dilated context encoding blocks, but also captures unique and scalable feature variation patterns in wavelet frequency domain via frequency pooling blocks. Afterwards, a deformable transformer detector on the extracted context features is built to accurately classify disease categories and locate regions, where a small set of key points are sampled, thus leading the detector to focus on informative feature subspace and accelerate convergence speed. Through comparative experiments on Vinbig Chest and Chest Det 10 datasets, CDT-CAD demonstrates its effectiveness in recognizing chest abnormities and outperforms 1.4% and 6.0% than the existing methods in AP(5)0 and AR on Vinbig dateset, and 0.9% and 2.1% on Chest Det-10 dataset, respectively.

关键词： Transformers X-ray imaging Feature extraction Location awareness Image segmentation Detectors Task analysis Abnormality detection chest x-ray images deformable transformer detector dilated context encoding block frequency pooling block iterative context-aware feature extractor

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Research on Liquidity Measurement and Influencing Factors of China Carbon Emission Allocation Trading Market

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Procedia Computer Science 2023年 221卷 1448-1457页

作者： Yong Shi Shuyang Peng Anqi Yu Kun Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Considering multi-dimensional concepts of liquidity, we introduces two classical liquidity measures to quantify the liquidity of China carbon emission allowance(CCEA) trading markets, and analyze their trading activities. Then monthly unbalanced panel was constructed based on daily trading data to further study on the influencing factors of CCEA market. We find that: 1) No matter what method we take, the Hubei pilot is the most liquid in the context of overall running periods, and the national carbon market and Guangdong pilot are also well performed compared to the other markets. 2)The foundation of national generalized market could bring about noticeable changes on the liquidity of carbon market, and Guangdong market which is one of the 8 carbon market pilots become more developed and liquid, while no significant variation in the rest of 7 pilots. 3) The emission control enterprises of each pilot have no inborn incentive to trade proactively, therefore transactions are concentrated on the performance cycle. 4)Regression results show that the price at the end of each month, trading volumes aggregated in each month, trading frequency of each month are all positively correlated with the liquidity, and return volatility of each month, carbon market fragmentation are negatively related to the liquidity. Finally we put forward some suggestion and policy recommendation rested on the above qualitative and quantitative analysis.

关键词： CEA,Carbon market,Liquidity,Measurement,Influencing factors

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Value of High-Quality Distribution in Front Warehouse Mode Retailing 8

Value of High-Quality Distribution in Front Warehouse Mode R...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Zhu, Jiayi Tian, Xin Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Front warehouse mode retailing originates from the e-commerce logistics industry, pre-positioning the warehouse and placing customer orders close to the community service station, so that goods can be delivered quickly. Consumers have put forward higher requirements for the timeliness of logistics distribution and the convenience of online purchases in the front warehouse mode. Using panel data of 37,362 transactions from 159 front warehouses in the online market, we study how consumers' satisfaction on products and logistics, as well as the punctuality of sorting and delivery affect sales performance. Our results show that negative review of products and logistics will significantly reduce retail sales performance. Interestingly, we find that punctual sorting and delivery increase the number of daily orders while reduce the average sales amount per order. When the punctuality of delivery service is guaranteed, consumers are willing to place orders more frequently even for some low-priced daily goods. This paper emphasizes the importance of the quality of delivery service in the front warehouse mode, and inspires retailers to give full play to the role of the front warehouse in meeting the daily needs of consumers for high-frequency and fast-moving consumer goods. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： front warehouse delivery service customer satisfaction sales performance retailing

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Rethink Video Retrieval Representation for Video Captioning

SSRN

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SSRN 2024年

作者： Tian, Mingkai Li, Guorong Qi, Yuankai Wang, Shuhui Sheng, Quan Z. Huang, Qingming School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computing Macquarie University Australia Key Laboratory of Intelligent Information Processing Institute of Computer Technology Chinese Academy of Sciences China

Video captioning, a challenging task targeting the automatic generation of accurate and comprehensive descriptions based on video content, has witnessed substantial success recently driven by bridging video representations and textual semantics. Inspired by the nature of the video retrieval task, which learns visual features strongly related to text queries, we propose to take advantage of visual representation learning from the video retrieval framework to tackle video captioning tasks. However, a simple direct application of video retrieval models performs poorly due to the weak ability to capture sufficient video details and temporal information required for video captioning. To increase the attention on details, we propose a learnable token shift module, which flexibly captures subtle movements in local regions across the temporal sequence. Furthermore, we devise a Refineformer, which learns to integrate local video tokens strongly related to desired captions via a cross-attention mechanism. Extensive experiments on MSVD, MSR-VTT and VATEX demonstrate the favorable performance of our method. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Price change prediction of Ultra high frequency financial data based on temporal convolutional network 8

Price change prediction of Ultra high frequency financial da...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Dai, Wei An, Yuan Long, Wen Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Through in-depth analysis of Ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are proposed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Temporal convolutional network Ultra high frequency Financial prediction

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Impact of IT Capability on Inventory management: An Empirical Study 8

Impact of IT Capability on Inventory Management: An Empirica...

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8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Tian, Xin Wang, Haoqing Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Despite the widespread adoption of technology in inventory management, empirical research that examines the effect of these technologies is scarce. Does IT capability really have a positive effect on the inventory management? This paper empirically studies the usage of information technology by the definition of IT capability. Our unique panel data set allows us to explore the relationship between technology usage and inventory management. Our results show that IT capability has a positive effect on the inventory strategy and inventory operation process, and has a negative effect on the out-of-stock level. Our research demonstrates the superiority of the usage of technology. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： intelligent warehousing business intelligence inventory management logistics

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A branch-and-bound algorithm for solving max-k-cut problem

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JOURNAL OF GLOBAL OPTIMIZATION 2021年第2期81卷 367-389页

作者： Lu, Cheng Deng, Zhibin North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Sch Econ & Management Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

The max-k-cut problem is one of the most well-known combinatorial optimization problems. In this paper, we design an efficient branch-and-bound algorithm to solve the max-k-cut problem. We propose a semidefinite relaxation that is as tight as the conventional semidefinite relaxations in the literature, but is more suitable as a relaxation method in a branch-and-bound framework. We then develop a branch-and-bound algorithm that exploits the unique structure of the proposed semidefinite relaxation, and applies a branching method different from the one commonly used in the existing algorithms. The symmetric structure of the solution set of the max-k-cut problem is discussed, and a strategy is devised to reduce the redundancy of subproblems in the enumeration procedure. The numerical results show that the proposed algorithm is promising. It performs better than Gurobi on instances that have more than 350 edges, and is more efficient than the state-of-the-art algorithm bundleBC on certain types of test instances.

关键词： Max-k-cut Semidefinite relaxation Branch-and-bound algorithm

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MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule mining

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2023年第9期35卷 9531-9546页

作者： Li, Yan Zhang, Chang Li, Jie Song, Wei Qi, Zhenlian Wu, Youxi Wu, Xindong Hebei Univ Technol Sch Econ & Management Tianjin 300400 Peoples R China North China Univ Technol Beijing 100144 Peoples R China Guangdong Eco Engn Polytech Guangzhou 510520 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300400 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from https://***/wuc567/Pattern-mining/tree/master/MCoR-Miner.

关键词： Sequential pattern mining sequential rule mining rule-antecedent co-occurrence pattern maximal rule mining

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