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Analysis on the Transmission of International Oil Price to China’s Price Index--Based on Granger Causality Network

Procedia Computer Science 2022年 199卷 1276-1283页

作者： Yijing Wang Xiuzhenzi Wang Kun Guo School of Economics and Management University of Chinese Academy of Science Beijing China Research Center on Fictitious Economy & Data Science Chinese Academy of Science Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

This paper uses complex network theory and Granger causality method to construct Granger Causality Network among price indices (PIGCN), specifically based on crude oil price and China’s domestic price index from a systematic perspective and provides a statistical physical method to analyze conduction fluctuations. This paper selects four typical price indices in China, including 36 sub price indices as samples for empirical research. Through the analysis of the PIGCN structure, we have analyzed the volatility transmission path of the international crude oil price and Chinese domestic price index, and further revealed the dynamic relationship among the price indices. Also, we have put forward some policy recommendations to better monitor the spread of price fluctuation risks.

关键词： Granger causality network Price transmission International crude oil price Price indices

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Climate change unequally affects nitrogen use and losses in global croplands

NATURE FOOD

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NATURE FOOD 2023年第4期4卷 294-+页

作者： Ren, Chenchen Zhang, Xiuming Reis, Stefan Wang, Sitong Jin, Jiaxin Xu, Jianming Gu, Baojing Zhejiang Univ Coll Environm & Resource Sci Hangzhou Peoples R China Zhejiang Univ Dept Land Management Hangzhou Peoples R China Zhejiang Univ Policy Simulat Lab Hangzhou Peoples R China Univ Melbourne Sch Agr & Food Melbourne Vic Australia UK Ctr Ecol & Hydrol Penicuik Scotland Univ Exeter Med Sch Knowledge Spa Truro England Univ Edinburgh Sch Chem Edinburgh Scotland Hohai Univ Coll Hydrol & Water Resources Nanjing Peoples R China Hohai Univ Key Lab Water Big Data Technol Minist Water Resour Nanjing Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Agr Resources & Environm Hangzhou Peoples R China Zhejiang Univ Minist Educ Key Lab Environm Remediat & Ecol Hlth Hangzhou Peoples R China

Quantification of the impact of climate change on crop yield and nitrogen use efficiency reflects global inequalities in agricultural N use and losses. Using long-term historical data from over 150 countries, this study reveals the importance of farm size to regulate N use for climate adaptation. Maintaining food production while reducing agricultural nitrogen pollution is a grand challenge under global climate change. Yet, the response of global agricultural nitrogen uses and losses to climate change on the temporal and spatial scales has not been fully characterized. Here, using historical data for 1961-2018 from over 150 countries, we show that global warming leads to small temporal but substantial spatial impacts on cropland nitrogen use and losses. Yield and nitrogen use efficiency increase in 29% and 56% of countries, respectively, whereas they reduce in the remaining countries compared with the situation without global warming in 2018. Precipitation and farm size changes would further intensify the spatial variations of nitrogen use and losses globally, but managing farm size could increase the global cropland nitrogen use efficiency to over 70% by 2100. Our results reveal the importance of reducing global inequalities of agricultural nitrogen use and losses to sustain global agriculture production and reduce agricultural pollution.

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Self-distillation and self-supervision for partial label learning

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PATTERN RECOGNITION 2024年 146卷

作者： Yu, Xiaotong Sun, Shiding Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China 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 UCAS MOE Social Sci Lab Digital Econ Forecasts & Polic Beijing 100190 Peoples R China

As a main branch of weakly supervised learning paradigm, partial label learning (PLL) copes with the situation where each sample corresponds to ambiguous candidate labels containing the unknown true label. The primary difficulty of PLL lies in label ambiguities, most existing researches focus on individual instance knowledge while ignore the importance of cross-sample knowledge. To circumvent this difficulty, an innovative multi-task framework is proposed in this work to integrate self-supervision and self-distillation to tackle PLL problem. Specifically, in the self-distillation task, cross-sample knowledge in the same batch is utilized to refine ensembled soft targets to supervise the distillation operation without using multiple networks. The auxiliary self-supervised task of recognizing rotation transformations of images provides more supervisory signal for feature learning. Overall, training supervision is constructed not only from the input data itself but also from other instances within the same batch. Empirical results on benchmark datasets reveal that this method is effective in learning from partially labeled data.

关键词： knowledge distillation Self-supervised learning Partial label learning Machine learning

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Nonparametric data-driven learning algorithms for multilocation inventory systems

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OPERATIONS RESEARCH LETTERS 2024年 57卷

作者： Zhong, Zijun He, Zhou City Univ Hong Kong Dept Mkt Kowloon Hong Kong Peoples R China Univ Chinese Acad Sci Sch Econ & Management 3 Zhongguancun Nanyitiao Peoples R China UCAS MOE Social Sci Lab Digital Econ Forecasts & Policy 3 Zhongguancun Nanyitiao Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China

We study a multilocation inventory system with unknown demand distribution using a nonparametric approach. The system consists of multiple distribution centers and customer locations, where products are shipped from the distribution centers to fulfill customer demands. We propose a novel algorithm, DMLI, for adaptive inventory management. Under specific conditions, we establish that the average expected T -period regret of DMLI converges to the optimal rate of O(1/ root T). (c) 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Multilocation inventory system Nonparametric learning Online convex optimization Regret

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Discovering the relationship between the number of film review topics and box office with NLP techniques 11

Discovering the relationship between the number of film revi...

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11th International Conference on Information Technology and Quantitative management, ITQM 2024

作者： Li, Bo Dai, Wei Liu, Shang Shi, Yong School of Finance and Economics Wuhan City Vocational College No. 10 Yezhihu West Road Hongshan District Wuhan430064 China PKU-WUHAN Institute for Artificial Intelligence No. 770 Gaoxin Avenue Hongshan District Wuhan430070 China School of Economics Shanghai University 99 Shangda Road BaoShan District Shanghai200444 China School of Economics and Management University of Chinese Academy of Sciences No. 3 Zhongguancun South Yitiao Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing100190 China

Numerous movie reviews on the internet can be used to analyze audience experiences of movies and provide inspiration for the operation and production of movies. In this work, we integrate and match the review data on Douban and the box office data on the website ***, and utilize the topic model in natural language processing to determine the number of each movie review topics, then establish an regression equation to analyze the relationship between the number of topics and movie box office. Our analysis shows a significant positive correlation between film box office and the number of movie review topics, and we have provided an explanation for this. Finally, we also analyzed the practical application value of this work. © 2024 The Authors.

关键词： Natural language processing systems

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Impact of China Railway Express on Urban Exports: Evidence from Prefectural-Level City Economic data 9th

Impact of China Railway Express on Urban Exports: Evidence f...

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Yan, Bochao Tian, Xin Deng, Shiyong Wu, Junfeng Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China China Railway Fifth Survey and Design Institute Group Co. Ltd. Beijing102600 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai200126 China

As a key project of infrastructure connectivity in the "Five-Pronged Approach"of strategic construction "The Belt and Road", the China Railway Express, has significantly improved the level of trade level in countries along the railway. This study uses an empirical model to correlate the container export data of China Railway Express from June 2020 to June 2021 with the export value and finds that the trade volume of China Railway Express is positively correlated with the export value of cities. The relationship between the operation of the China Railway Express and the export trade of cities that enjoy the rail service is rigorously explained through the actual data of the operation of the China Railway Express provided by the enterprises. The empirical results further prove the positive impact of the China Railway Express on cities that enjoy the rail service. © 2022 The Authors. Published by Elsevier B.V.

关键词： Commerce

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Penetration Effect of Informatization on Technological Progress in Logistics Industry: Empirical Evidence from Inter-provincial Panel data in China 9th

Penetration Effect of Informatization on Technological Progr...

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Gong, Yaling Tian, Xin Jiang, Zuoliang Jiao, Yamin School of Economics and Management Yuzhang Normal University Nanchang330103 China 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 Shanghai Heading Information Engineering Co. Ltd. Shanghai201112 China Shanghai Winjoin Information Technology Co. Ltd. Shanghai200126 China

This paper investigates pervasiveness effect of information technology on the technological progress of logistics in China. Using panel data of 30-regions in China during 2008 to 2020, we propose both static and dynamic panel data models, and find both resource level and spreading rate of information technology have negative influences on technological progress of logistics. The resource level of information technology has a short-term effect on technological progress of logistics, whereas the spreading rate of information technology has a long-term effect. Moreover, the result reveals that the discordant problem of information construction of logistics between resource level and spreading rate cause the negative influence of information technology. © 2022 The Authors. Published by Elsevier B.V.

关键词： Industry 4.0

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Visual Anomaly Detection for Images: A Systematic Survey 8

Visual Anomaly Detection for Images: A Systematic Survey

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

作者： Yang, Jie Xu, Ruijie Qi, Zhiquan Shi, Yong Univ Chinese Acad Sci Sch Comp & Technol Beijing 101408 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 101408 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 Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. This problem has attracted a considerable amount of attention in relevant research communities. Especially in recent years, the development of deep learning has sparked an increasing interest in the visual anomaly detection problem and brought a great variety of novel methods. In this paper, we provide a comprehensive survey of the classical and deep learning-based approaches for visual anomaly detection in the literature. We group the relevant approaches in view of their underlying principles and discuss their assumptions, advantages, and disadvantages carefully. We aim to help the researchers to understand the common principles of visual anomaly detection approaches and identify promising research directions in this field. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Visual anomaly visual anomaly detection deep learning

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BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation 35

BernNet: Learning Arbitrary Graph Spectral Filters via Berns...

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35th Conference on Neural Information Processing Systems (NeurIPS)

作者： He, Mingguo Wei, Zhewei Huang, Zengfeng Xu, Hongteng Renmin Univ China Beijing Peoples R China Fudan Univ Shanghai Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Method MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China

ISBN: (纸本)9781713845393

Many representative graph neural networks, e:g:, GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters. However, existing work either applies predefined filter weights or learns them without necessary constraints, which may lead to oversimplified or ill-posed filters. To overcome these issues, we propose BernNet, a novel graph neural network with theoretical support that provides a simple but effective scheme for designing and learning arbitrary graph spectral filters. In particular, for any filter over the normalized Laplacian spectrum of a graph, our BernNet estimates it by an order-K Bernstein polynomial approximation and designs its spectral property by setting the coefficients of the Bernstein basis. Moreover, we can learn the coefficients (and the corresponding filter weights) based on observed graphs and their associated signals and thus achieve the BernNet specialized for the data. Our experiments demonstrate that BernNet can learn arbitrary spectral filters, including complicated band-rejection and comb filters, and it achieves superior performance in real-world graph modeling tasks. Code is available at https://***/ivam- he/BernNet.

关键词： Graph neural networks

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Stream Concept-cognitive Computing System for Streaming data Learning

TechRxiv

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TechRxiv 2023年

作者： Mi, Yunlong Liu, Wenqi Shi, Yong The Big Data and Intelligent Decision Making Research Center Central South University Changsha410083 China The Data Science Research Center Kunming University of Science and Technology Kunming650500 China The Faculty of Science Kunming University of Science and Technology Kunming650500 China The School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

People can often acquire knowledge dynamically and rapidly from different types of data, yet existing incremental learning algorithms are still computationally time consuming and most of stream learning methods are mainly designed for streaming data while ignoring other types of data. Hence, this paper proposes a novel dynamic concept learning (CL) algorithm by imitating human cognitive learning processes from the perspective of brain logical cognition, which is named stream concept-cognitive computing system (streamC3S). For streamC3S, it mainly consists of three aspects: the concept space, CL process, and model update process. Moreover, considering the concept drift frequently occurs in the streaming data over time, an extended version of streamC3S (namely, streamC3SE) is also proposed in this work. Specifically, we first show the related theories for streamC3S and streamC3SE on the basis of the concept space. Then an overall framework and its corresponding algorithm are shown. Finally, experimental results on various types of datasets, including the standard machine learning datasets, streaming datasets, image datasets, and two traffic data streams, validate the effectiveness of our streamC3S and streamC3SE compared to the state-of-the-art incremental learning and stream learning algorithms. © 2023, CC BY.

关键词： Learning algorithms

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