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The Effect of Industrial Structure Change on Carbon Dioxide Emissions: A Cross-Country Panel Analysis

Journal of Systems Science and Information 2020年第1期11卷 1-16页

作者： Jichang DONG Jing HE Xiuting LI Xindi MOU Zhi DONG School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Sino-Danish College University of Chinese Academy of SciencesBeijing 100190China

Reduction of carbon dioxide(CO2)emissions is one of the biggest challenges for global sustainable development,in which economic growth characterized by industrialization plays a formidable *** innovatively adopted the input and output(I-O)table of 41 countries released by World I-O database to determine the industrial structure change and analyze its impact on CO2 emission evolution by developing a cross-country panel *** empirical results show that industrial structure change has a significantly negative effect on CO2 emissions;to be specific,0.1 unit increase in the linkage of manufacturing sector and service sector will lead to a decrease of 0.94 metric tons per capita CO2 emissions,indicating that upgrading industrial structure contributes to carbon mitigation and sustainable ***,urbanization,technology and trade openness have significantly negative impact on CO2 emissions,while economy growth and energy use take positive *** particular,a 1%increase in per capita income will contribute to an increase of 8.6 metric tons per capita CO2 ***,the effect of industrial structure on environment degradation is moderated by technology *** findings fill the gaps of previous literature and provide valuable references for effective policies to mitigate CO2 emissions and achieve sustainable development.

关键词： CO2 emissions reduction industrial structure change input and output table panel model cross-country policy analysis

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Projection-based Consistent Test for Linear Regression Model with Missing Response and Covariates

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Acta Mathematicae Applicatae Sinica 2020年第4期36卷 917-935页

作者： Su-jin ZHENG Si-yu GAO Zhi-hua SUN China Institute for Actuarial Science/School of Insurance Central University of Finance and EconomicsBeijing 100081China School of Mat hematic Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management of Chinese Academy of Sciences Beijing 100049China

In recent years,there has been a large amount of literature on missing *** of them focus on situations where there is only missingness in response or *** this paper,we consider the adequacy check for the linear regression model with the response and covariates missing *** apply model adjustment and inverse probability weighting methods to deal with the missingness of response and covariate,*** order to avoid the curse of dimension,we propose an empirical process test with the linear indicator weighting *** asymptotic properties of the proposed test under the null,local and global alternative hypothe tical models are rigorously investigated.A consisten t wild boot strap method is developed to approximate the critical ***,simulation studies and real data analysis are performed to show that the proposed method performed well.

关键词： consistency linear indicator weighting function empirical process missing response and covariates projection

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Mvqs: Robust Multi-View Instance-Level Cost-Sensitive Learning Method for Imbalanced data Classification

SSRN

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SSRN 2023年

作者： Hou, Zhaojie Tang, Jingjing Li, Yan Fu, Saiji Tian, Yingjie School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Institute of Big Data Southwestern University of Finance and Economics Chengdu611130 China School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 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 MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS Beijing100190 China

During multi-view imbalanced learning, data is collected from different sources and class labels are heavily skewed. Multi-view imbalanced learning has been studied extensively, with two main categories: multi-view cost-sensitive learning and multi-view ensemble learning. However, those methods are subject to certain limitations: 1) omission of either consensus or complementary information, 2) complex preprocessing of multi-view ensemble learning and manual assignment of misclassification costs of multi-view cost-sensitive learning, and 3) limited ability to handle noisy data. Therefore, we aim to design a concise and unified framework to grapple with the multi-view representations, imbalanced classes and noisy samples simultaneously. Inspired by the elegant metrics of SVM-style models and asymmetric quadratic type squared error loss function (QTSELF), we propose a robust multi-view instance-level cost-sensitive support vector machine for imbalanced data classification called MVQS. The consensus regularization term and combination weight strategy are employed to fully exploit multi-view information. The QTSELF loss can not only adaptively impose instance-level penalties to samples, but also make MVQS more robust to noisy samples. We solve MVQS with the gradient descent and the alternating direction method of multipliers algorithms. The comprehensive experimental results on various datasets validate that MVQS is more competitive and robust than other benchmark approaches. © 2023, The Authors. All rights reserved.

关键词： Support vector machines

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How Do Climate Factors Affect Household Portfolio Choices? Empirical Evidence from China's Household Survey

SSRN

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SSRN 2021年

作者： Zhao, Wen Li, Xiuting Liu, Xiaoting Guo, Sijia Dong, Zuoji 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

This paper empirically investigates the impacts of climate factors, including temperature and precipitation, on household portfolio choices based on the panel data of 2,110 households continuously observed from the China Family Panel Studies (CFPS) for 2010, 2012, 2014 and 2016. The results show that climate factors significantly affect household portfolio choices mainly through channels including income, asset prices, health and risk attitudes. Furthermore, the results also show that climate factors moderate the effects of income, asset prices and investors’ confidence on household portfolio choices. This research contributes to the literature by introducing theories regarding behavioral finance and climate economics into the research on household portfolio choices, which provides insights into the impact mechanisms of climate factors on household behaviors and enriches the studies of behavioral investment decision making and environmental economics. © 2021, The Authors. All rights reserved.

关键词： Investments

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The Effect of Online Channel Addition on Store Performance:Empirical Evidence from Chinese Chain Retailers

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Journal of Systems Science and Information 2021年第4期9卷 356-377页

作者： Yixin WANG Qiguo GONG Tingyan WANG Xin TIAN School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Shanghai HEADING Information Engineering Co. LtdShanghai 201112China Key Labomtory of Big Data Mining and Knowledge Managements Chinese Academy of SciencesBeijing 100190China Nuffield Department of Medicine University of OxfordSouth Parks RoadOxford OXl 3SYUnited Kingdom NIHR Oxford Biomedical Research Centre Big Data InstituteUniversity of OxfordOld Road CampusOxford OX37LFUnited Kingdom Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China

With the rapid development of e-Commerce and takeaway platforms, retailers have gradually developed multi-channel operations. However, limited empirical studies explored the effects of an online channel offered by takeaway platforms on the store performance. Does an online channel addition have a synergy effect or a cannibalization effect on store performance? We empirically investigate these effects by analyzing a large dataset including diverse samples collected from multiple retailers across various regions. The dataset includes 2115 stores across 25 retailers for 10 months that includes two types of retail formats and covers 16 provinces and 21 cities of China. We study the impacts of the newly introduced online channel on the incumbent offline channel and the overall store *** empirical results reveal that the online channel addition mainly has a synergy effect. Specifically,for the overall store, it has a positive effect on the sales and product variety, whereas it has a negative effect on the basket size. Surprisingly, an online channel addition also has a positive effect on the offline sales. Our study adds novel values to multi-channel retailing literature by empirically researching the cannibalization and synergy effect of a new type of online channel, offered by takeaway platforms. It can provide insights for retail enterprises who are interested in introducing O2 O model.

关键词： online channel addition cannibalization effect synergy effect propensity score matching(PSM) different-in-different(DID)method

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The Further Development of Intelligent knowledge for Wisdom

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Procedia Computer Science 2022年 214卷 1382-1388页

作者： Jifa Gu Xiao Xiao Lingling Zhang Academy of Mathematics and Systems Science of Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing 100190 China

This paper introduces some modes from D, I, K to W, M. From different angles we investigate the various process. Especially we utilize the intelligent knowledge to develop the whole process. Finally we design a new medical hall for workshop of meta-synthetic engineering for helping patient to get the appropriate treatment and useful knowledge for health.

关键词： Wisdom Intelligent knowledge DIKWM Medical Hall for Workshop of Meta-synthetic Engineering

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The neural network methods for solving Traveling Salesman Problem

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Procedia Computer Science 2022年 199卷 681-686页

作者： Yong Shi Yuanying Zhang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and 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 College of Information Sciences and Technology University of Nebraska at Omaha NE 68182 USA School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100190 China

Traveling Salesman Problem(TSP) is a main attention issue at present. Neural network can be used to solve combinatorial optimization problems. In recent years, there have existed many neural network methods for solving TSP, which has made a big step forward for solving combinatorial optimization problems. This paper reviews the neural network methods for solving TSP in recent years, including Hopfield neural network, graph neural network and neural network with reinforcement learning. Using neural network to solve TSP can effectively improve the accuracy of the approximate solution. Finally, we put forward the prospect of solving TSP in the future.

关键词： Traveling Salesman Problem Neural network Hopfield neural network Graph neural network Reinforcement learning

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Visual Anomaly Detection for Images: A Systematic Survey

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Procedia Computer Science 2022年 199卷 471-478页

作者： Jie Yang Ruijie Xu Zhiquan Qi Yong Shi School of Computer and Technology University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and 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 School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China College of Information Science and Technology University of Nebraska at 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.

关键词： Visual anomaly visual anomaly detection deep learning

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Learning Unified Embeddings for Recommendation via Meta-path Semantics 21

Learning Unified Embeddings for Recommendation via Meta-path...

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29th ACM International Conference on Multimedia, MM 2021

作者： Hao, Qianxiu Xu, Qianqian Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781450386517

Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years. As a typical instantiation of HINs, meta-path is introduced in search of higher-level representations of user-item interactions. Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance;2) the representative power is limited by shallow/stage-wise formulations. To solve these issues, we propose an end-to-end and unified embedding-based recommendation framework with graph-based learning. To address 1), we propose a flexible fusion module to integrate meta-path-based similarities into relative similarities between users and items. To address 2), we take advantage of the powerful representative ability of deep neural networks to learn more complicated and flexible latent embeddings. Finally, empirical studies on real-world datasets demonstrate the effectiveness of our proposed method. © 2021 ACM.

关键词： Embeddings

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ReconBoost: Boosting Can Achieve Modality Reconcilement

arXiv

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arXiv 2024年

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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