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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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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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Multiple factor flows and heterogeneous premium of urban house prices: Empirical evidence from Chinese cities

引用

Finance Research Letters 2024年 69卷

作者： Huang, Xirong Geng, Chen Li, Xiuting Dong, Jichang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences es Beijing 100190 China

Notable Regional heterogeneity is commonly observed in housing markets. This study examines how multiple factor flows affect urban house prices and the resulting premium. It utilizes over 100 million pieces of Chinese intercity factor flow data to construct a multi-layer network and introduces a new metric called multiple connectivity quality to measure a city's comprehensive factor connection capability. Empirical analysis reveals that cities with higher multiple connectivity quality tend to exhibit higher price premiums, making them more resilient to market fluctuations. This study offers fresh insight into regional heterogeneity within housing markets and holds practical significance for stabilizing these markets. © 2024 The Author(s)

关键词： Heterogeneous premiums House prices Multi-layer network Multiple factor flows Panel data model

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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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Deep Transformation Model

arXiv

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arXiv 2024年

作者： Wang, Tong Zhang, Shunqin Zhang, Sanguo Huang, Jian Ma, Shuangge Department of Biostatistics Yale School of Public Health New HavenCT United States School of Mathematical Sciences University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong

There has been a significant recent surge in deep neural network (DNN) techniques. Most of the existing DNN techniques have restricted model formats/assumptions. To overcome their limitations, we propose the nonparametric transformation model, which encompasses many popular models as special cases and hence is less sensitive to model mis-specification. This model also has the potential of accommodating heavy-tailed errors – a robustness property not broadly shared. Accordingly, a new loss function, which fundamentally differs from the existing ones, is developed. For computational feasibility, we further develop a double rectified linear unit (DReLU)based estimator. To accommodate the scenario with a diverging number of input variables and/or noises, we propose variable selection based on group penalization. We further expand the scope to coherently accommodate censored survival data. The estimation and variable selection properties are rigorously established. Extensive numerical studies, including simulations and data analyses, establish the satisfactory practical utility of the proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Weakly Supervised Video Individual Counting

Weakly Supervised Video Individual Counting

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xinyan Liu Guorong Li Yuankai Qi Ziheng Yan Zhenjun Han Anton van den Hengel Ming-Hsuan Yang Qingming Huang University of Chinese Academy of Science Key Lab of Big Data Mining and Knowledge Management UCAS China Macquarie University Australian Institute for Machine Learning The University of Adelaide University of California at Merced Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. Existing methods learn representations based on trajectory labels for individuals, which are annotation-expensive. To provide a more realistic reflection of the underlying practical challenge, we introduce a weakly supervised VIC task, wherein trajectory labels are not provided. Instead, two types of labels are provided to indicate traffic entering the field of view (inflow) and leaving the field view (outflow). We also propose the first solution as a baseline that formulates the task as a weakly supervised contrastive learning problem under group-level matching. In doing so, we devise an end-to-end trainable soft contrastive loss to drive the network to distin-guish inflow, outflow, and the remaining. To facilitate future study in this direction, we generate annotations from the existing VIC datasets Sense Crowd and CroHD and also build a new dataset, UAVVIC. Extensive results show that our baseline weakly supervised method outperforms supervised methods, and thus, little information is lost in the transition to the more practically relevant weakly supervised task. The code and trained model can be found at CGNet.

关键词： Computer vision Costs Codes Annotations Contrastive learning Drives Reflection

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Explicit Unsupervised Feature Selection Based on Structured Graph and Locally Linear Embedding

SSRN

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SSRN 2023年

作者： Miao, Jianyu Zhao, Jingjing Yang, Tiejun Fan, Chao Tian, Yingjie Shi, Yong Xu, Mingliang School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 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 School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

Numerous redundant and irrelevant features, and outliers are usually contained in high-dimensional data whose presence, if ignored, can bring detrimental effects on the performance of data processing tasks. Feature selection as a trending topic in a great deal of domains has been greatly concerned, which can effectively deal with noise and outlier. In this work, we propose a novel unsupervised method that unifies the explicit feature selective matrix learning and structured graph learning into a framework. An explicit selection matrix only consisting of 0 and 1 are designed, which can be integrated into locally linear embedding, and can be effectively and efficiently solved with closed-form solution. Furthermore, we take into account the local consistency between the samples so that the low dimensional manifold structures of samples can be preserved to a better degree. Instead of using the pre-defined similarity matrix to construct graph, we make use of the way of learning to obtain the adaptive graph. Besides, for the purpose of exploiting the cluster structure of data, we impose a rank constraint on the graph. Notably, both explicit selection matrix and intrinsic structure are learned in low-dimensional feature subspace, and thus the proposed methodology is more straightforward. We present an effective and efficient algorithm for the resulting problem based on alternative optimization strategy, and establish its convergence and computational complexity analysis results. Extensive experiments verify that our proposed approach can significantly outperform advanced methods. © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Overview of Essential Components in deep learning reference-based super resolution methods

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Procedia Computer Science 2024年 242卷 1243-1248页

作者： Jiayu Xue Junjie Liu Yong Shi School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 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 Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Reference-based super resolution (RefSR) aims to recover the lost details in a low-resolution image and generate a high-resolution result, guided by a high-resolution reference image with similar contents or textures. In contrast to the traditional single-image super-resolution, which focuses on the intrinsic properties of the single low-resolution image, the challenge of RefSR lies in matching and aggregating highly-related but misaligned reference images with low-resolution images. Several effective but complex designs have been proposed to address this challenge, which poses difficulties in implementing RefSR in real-world applications. In order to better understand the working mechanism of RefSR and design a more efficient and lightweight architecture, we provide a review about the essential components of the existing deep learning-based RefSR. We decompose and classify the common pipeline into four submodules according to the functionalities. Then, we summarize and describe the implementation details of the commonly-adopted approaches in each submodule. Finally, we discuss the challenges and promising research directions.

关键词： Reference-based super resolution Deep learning Feature matching Feature alignment

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MambaLCT: Boosting Tracking via Long-term Context State Space Model

arXiv

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arXiv 2024年

作者： Li, Xiaohai Zhong, Bineng Liang, Qihua Li, Guorong Mo, Zhiyi Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Colleges Universities Key Laboratory of Intelligent Software Wuzhou University Wuzhou543002 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100101 China

Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing work is limited, only considering object information from adjacent frames or video clips, leading to insufficient utilization of contextual information. To address this issue, we propose MambaLCT, which constructs and utilizes target variation cues from the first frame to the current frame for robust tracking. First, a novel unidirectional Context Mamba module is designed to scan frame features along the temporal dimension, gathering target change cues throughout the entire sequence. Specifically, target-related information in frame features is compressed into a hidden state space through selective scanning mechanism. The target information across the entire video is continuously aggregated into target variation cues. Next, we inject the target change cues into the attention mechanism, providing temporal information for modeling the relationship between the template and search frames. The advantage of MambaLCT is its ability to continuously extend the length of the context, capturing complete target change cues, which enhances the stability and robustness of the tracker. Extensive experiments show that long-term context information enhances the model’s ability to perceive targets in complex scenarios. MambaLCT achieves new SOTA performance on six benchmarks while maintaining real-time running speeds. Code and models are available at https://***/GXNU-ZhongLab/MambaLCT. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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