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

作者： Tang, Anda Yao, Tongsheng Niu, Lingfeng Shi, Yong School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Deep neural networks (DNNs) is very important and have achieved remarkable accuracies in tasks such as image processing. However, the success of DNNs heavily relies on excessive computation and parameter storage costs. To cut down the overheads, a wide range of regularization terms are proposed on netwok compression, which have their own scope of application though. To further reduce computation, structural sparsity learning is a matter of great concern where group sparse regularizations are blossoming in radiant splendour. However, the group sparse regularizations used in network compression is relatively scarce. A majority of structural compression only stem from 2,1 regularization. In addition, sparse regularization in structural network compression lacks unified form and theoretical guidance. Therefore, we focus our attention on a generalized sparse regularization in sparse learning of deep learning. We put a series of sparse regularization into a framework where some group sparse regularizations with different properties are introduced. The transformation of regularizations can be completed through the selection of hyper-parameters. In this case, we can use the optimization strategy for unification and theoretical guidance, and transform different sparse regularizations to adapt to different tasks. To our knowledge, it is the first work applying the generalized sparse regularization with novel group sparsity in compression of DNNs. The proposed p,q,r regularization can achieve both neuron-level and connection-level sparsity. And we give the analytical solutions for some specific (p,q, r) thus the compression can be achieved throughout the standard optimization process. We perform extensive experiments to illustrate the advantages and characteristics of our new method for further applications. © 2022 The Author(s).

关键词： Deep neural networks

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New technology foresight method based on intelligent knowledge management

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Frontiers of Engineering management 2020年第2期7卷 238-247页

作者： Lingling ZHANG Siting HUANG School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Key Laboratory of Big Data Mining and Knowledge ManagementChinese Academy of SciencesBeijing 100190China School of Economics and Management University of Chinese Academy

The increasing importance of technology foresight has simultaneously raised the significance of methods that determine crucial areas and ***,qualitative and quantitative methods have *** former involve high costs and many limitations,while the latter lack expert *** knowledge management emphasizes human–machine integration,which combines the advantages of expert experience and data ***,we proposed a new technology foresight method based on intelligent knowledge *** method constructs a technological online platform to increase the number of participating experts.A secondary mining is performed on the results of patent analysis and ***,forward-looking,innovative,and disruptive areas and relevant experts must be discovered through the following comprehensive process:Topic acquisition→topic delivery→topic monitoring→topic guidance→topic reclamation→topic sorting→topic evolution→topic conforming→expert recommendation.

关键词： technology foresight intelligent knowledge management technological online platform

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Measuring China's Real Estate Financial Innovation from the Perspective of Government,Enterprises and the Public:Index Compilation and Its Spatial-Temporal Characteristics Analysis

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Journal of Systems Science and Information 2023年第1期11卷 1-34页

作者： Jichang DONG Lijun YIN Xiaoting LIU Xiuting LI 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 China Construction Second Engineering Bureau Co.Ltd Beijing 100070China

In recent years,China has witnessed the rapid development in housing finance,and there have emerged constantly real estate finance innovations;however,there exists no relevant index for measuring the innovations of China's real estate *** on the perspectives of the governments,enterprises and the public,this paper constructs the"innovation index of real estate finance"on a quarterly basis from 2009 to 2019,with the method of empowerment which combines the subjective method(analytic hierarchy process)and the objective one(range coefficient method).It clearly and concretely depicts the innovations in housing finance and the related temporal-spatial characteristics in China since the outbreak of the financial crisis in *** index covers 30 provinces,autonomous regions and municipalities directly under the central government,and analyzes its temporal and spatial *** findings show that there exist a strong spatial autocorrelation and a big regional difference in innovations.

关键词： governments enterprises public real estate finance innovation

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Types and Evolutionary Boundaries of Agents 5

Types and Evolutionary Boundaries of Agents

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Liu, Feng Liu, Ying Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Economics and Management Beijing China

ISBN: (纸本)9781665426305

Through the analysis of the characteristics of natural intelligent systems, the three laws of intelligence are exploringly proposed. The most important point is that any agent is a system with knowledge input, output, (dynamic) storage and creation abilities Since an agent has four abilities, and each ability has three states, including zero, infinity, and finite (between zero and infinity), 81 types of agents can be formed. Therefore, it can be inferred that Alpha point (No. 1), where all the four abilities of an agent are zero, and Omega point (No. 81), where all four abilities are infinite are the boundary of the evolution of Agent. This paper designs experiments to detect these 81 agents, and finds that 11 of them can be confirmed by experiments at present. Through the discussion of the states of Agents No. 64 to No. 81, fresh thinking is proposed for the generation and realization of the paradox. © 2021 IEEE.

关键词： Evolution of Agents Types of Agents α Point Ω Point

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Build a Tourism-Specific Sentiment Lexicon Via Word2vec

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Annals of data Science 2018年第1期5卷 1-7页

作者： Li, Wei Zhu, Luyao Guo, Kun Shi, Yong Zheng, Yuanchun School of Economics and Management University of Chinese Academy of Sciences Beijing China Fictitious Economy and Data Science Research Center Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Online travel and online travel culture developed fast in China recently years while useful knowledge still hidden under a large number of tourism reviews. Therefore, we need effective sentiment analysis methods to mine useful knowledge which can help tourism websites make decisions and improve their travel products. Some data-driven sentiment lexicons have poor performance on sentiment polarity classification due to lack of semantic information. Thus, we propose an effective and more proper data-driven sentiment lexicon construction method incorporating manually labeled sentiment scores, semantic similarity information that is introduced by machine learning method word2vec. Experimental results demonstrate that our method improves the performance of tourism sentiment analysis significantly. © 2018, Springer-Verlag GmbH Germany.

关键词： Sentiment analysis

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Pyramid scheme in stock market:A kind of financial market simulation

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Chinese Physics B 2021年第9期30卷 624-632页

作者： Yong Shi Bo Li Guang-Le Du School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China

Artificial stock market simulation based on agent is an important means to study financial *** on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid *** simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main *** work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.

关键词： pyramid scheme agent-based model artificial stock market herd behavior

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Research on Pricing of data Based on Bi-level Programming Model

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Annals of data Science 2024年第4期11卷 1391-1419页

作者： Ding, Yurong Tian, Yingjie 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 at UCAS Beijing100190 China

Effective value measurement and pricing methods can greatly promote the healthy development of data sharing, exchange and reuse. However, the uncertainty of data value and neglect of interactivity lead to information asymmetry in the transaction process. A perfect pricing system and well-designed data trading market (hereafter called data market) can widely promote data transactions. We take the three-agents data market as an example to construct a sound data trading process. The data owner who provides data records, the model buyer who is interested in buying machine learning (ML) model instances, and the data broker who interacts between the data owner and the model buyer. Based on the characteristics of data market, like truthfulness, revenue maximization, version control, fairness and non-arbitrage, we propose a data pricing methods based on different model versions. Firstly, we utilize market research and construct a revenue maximization (RM) problem to price the different versions of ML models and solve it with the RM-ILP process. However, the RM model based on market research has two major problems: one is that the model buyer has no incentive to tell the truth, that is, the model buyer will lie in the market research to obtain a lower model price;the other is that it asks the data broker to release version menu in advance, resulting in an inefficient operation of the data market. In view of the defects of the RM transaction model, we propose a model buyers behavior analysis, establish the revenue maximization function based on different data versions to establish a bi-level linear programming model. We further add the incentive compatibility constraint and the individual rationality constraint, taking the utility of the model buyer and the revenue of the data broker into account. This reflects the consumer driven model in the data transaction mode. Finally, the RM-BLP process is proposed to transform RM problem into an equivalent single-level integer program

关键词： Costs

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A data-Driven Dynamic Programming Model for Research Position Demand Forecasting

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Annals of data Science 2017年第1期4卷 19-30页

作者： Xie, Yongjia Wu, Dengsheng Chen, Yuanping Jiao, Wenbin Li, Jianping Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

It has been worthy of notice that the number of scientific researchers has experienced a rapid growth in China. Meanwhile, the strict restriction to the total number and the position structure of researchers has exerted great pressure on the Chinese researchers. The decision makers have noticed this dilemma and a quantitative predicting result for decision support is in need. This paper puts forward a data-driven dynamic programming model to estimate the research position demand gap based on the thought of dynamic programming. This model fully considers the real practice of human resource management in scientific management in China. In the empirical study, the personnel data from 2006 to 2014, which are abstracted from the Academia Resource Planning system of the Chinese Academy of Sciences, are applied to the empirical analysis to estimate the human resource demand gap in the 13th Five Year Plan. The results show that there is a big demand gap of the research position on the whole in the next five years. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Dynamic programming

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COMPREHENSIVE ANALYSIS OF OVER-SMOOTHING IN GRAPH NEURAL NETWORKS FROM MARKOV CHAINS PERSPECTIVE

arXiv

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arXiv 2022年

作者： Zhao, Weichen Wang, Chenguang Han, Congying Guo, Tiande Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing China

The over-smoothing problem is an obstacle of developing deep graph neural network (GNN). Although many approaches to improve the over-smoothing problem have been proposed, there is still a lack of comprehensive understanding and conclusion of this problem. In this work, we analyze the over-smoothing problem from the Markov chain perspective. We focus on message passing of GNN and first establish a connection between GNNs and Markov chains on the graph. GNNs are divided into two classes of operator-consistent and operator-inconsistent based on whether the corresponding Markov chains are time-homogeneous. Next we attribute the over-smoothing problem to the convergence of an arbitrary initial distribution to a stationary distribution. Based on this, we prove that although the previously proposed methods can alleviate over-smoothing, but these methods cannot avoid the over-smoothing problem. In addition, we give the conclusion of the over-smoothing problem in two types of GNNs in the Markovian sense. On the one hand, operator-consistent GNN cannot avoid over-smoothing at an exponential rate. On the other hand, operator-inconsistent GNN is not always over-smoothing. Further, we investigate the existence of the limiting distribution of the time-inhomogeneous Markov chain, from which we derive a sufficient condition for operator-inconsistent GNN to avoid over-smoothing. Finally, we design experiments to verify our findings. Results show that our proposed sufficient condition can effectively improve over-smoothing problem in operator-inconsistent GNN and enhance the performance of the model. © 2022, CC BY-NC-SA.

关键词： Message passing

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Negative Overnight Returns: China's Security Markets

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Procedia Computer Science 2015年 55卷 980-989页

作者： Qingyuan Liu Hongbo Guo Xianhua Wei Key Laboratory of Big Data Mining and Knowledge Management School of Management University of Chinese Academy of Sciences Beijing 100190 China

We find that there exist statistically significant negative overnight returns in China's security markets, which is totally different from the previous research on HS300 Index by He et al. (2013), and the negative overnight returns are comparatively larger in China's GEM (Growth Enterprise Market) board and SME (Small and Medium Enterprise) board than in the mainboards of Shanghai and Shenzhen security markets. We also find some of the SWS Primary Sectors have negative overnight returns after ticking out of market effects, which can be a great guide for investing in hedging portfolios of specific sectors.

关键词： Negative overnight returns HS300 Index Hedging portfolios SSE50 Index SWS Primary Sector

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