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Active and Passive Factors of Oil Prices

Journal of Donghua University(English Edition) 2015年第2期32卷 284-287页

作者：海豹申立勇 School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Nanagement Chinese Academy of Sciences

Price volatility analysis is a basic problem in the price modification,financial risk estimation and management *** the global commodities,oil plays an important role in the development of modern industry and *** the price of crude oil analysis is a hot *** is also a difficult topic since there are so many factors associating the price *** some factors give the different influences in the different *** on data computing,people generally classify the factors into positive and negative *** some factors do not affect the price as the nominal *** instance,the output of OPEC gave the positive contributions to the oil price in the past long ***,the investigation of the historic WTI oil price is well proposed and the factors are classified into active and passive *** then the better explanations are given using this type of classification.

关键词： instance classify nominal classified volatility expectation contributions crude Correlation normalized

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ANALYSIS OF THE PREDICTION CAPABILITY OF WEB SEARCH data BASED ON THE HE-TDC METHOD - PREDICTION OF THE VOLUME OF DAILY TOURISM VISITORS

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Journal of Systems Science and Systems Engineering 2017年第2期26卷 163-182页

作者： Geng Peng Ying Liu Jiyuan Wang Jifa Gu School of Economic 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 Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

Web search query data are obtained to reflect social spots and serve as novel economic indicators. When faced with high-dimensional query data, selecting keywords that have plausible predictive ability and can reduce dimensionality is critical. This paper presents a new integrative method that combines Hurst Exponent （HE） and Time Difference Correlation （TDC） analysis to select keywords with powerful predictive ability. The method is called the HE-TDC screening method and requires keywords with predictive ability to satisfy two characteristics, namely, high correlation and fluctuation memorability similar to the predicting target series. An empirical study is employed to predict the volume of tourism visitors in the Jiuzhai Valley scenic area. The study shows that keywords selected using HE-TDC method produce a model with better robustness and predictive ability.

关键词： Tourism visitor volume prediction web-search data HE-TDC method Jiuzhai Valley,time series Hurst exponent

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An Efficient Slap Fingerprint Segmentation Algorithm Based on Convnets and Knuckle Line 12th

An Efficient Slap Fingerprint Segmentation Algorithm Based o...

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12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Tang, Siqi Qin, Jin Liu, Yan Han, Congying Guo, Tiande Beijing China Key Laboratory of Big Data Mining and Knowledge Management School of Mathematical Science UCAS Beijing China

ISBN: (纸本)9783319699226

We propose a novel and efficient technique to extract individual fingerprints from a slap-image and identify them into their corresponding indices i.e. index, middle, ring or little finger of left/right hand. We pose the orientation of the hand to a classification problem, and present an approach based on Convolutional Neural Networks (ConvNets) to address the angle of the hand. Geometrical and spatial properties of hand are applied to split a single finger and detect the knuckle line. The proposed algorithm solves the challenges of segmentation like the large rotational angles of the hand and non-elliptical shape of components. Extensive experimental evaluations demonstrate the success of this approach. © 2017, Springer International Publishing AG.

关键词： Convolution

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Determination and(Re)Parametrization of Rational Developable Surfaces

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Journal of Systems Science & Complexity 2015年第6期28卷 1426-1439页

作者： SHEN Liyong PREZ-DíAZ Sonia School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge management Chinese Academy of Sciences Dpto de Física y Matemáticas Universidad de Alcalá

The developable surface is an important surface in computer aided design, geometric modeling and industrial manufactory. It is often given in the standard parametric form, but it can also be in the implicit form which is commonly used in algebraic geometry. Not all algebraic developable surfaces have rational parametrizations. In this paper, the authors focus on the rational developable surfaces. For a given algebraic surface, the authors first determine whether it is developable by geometric inspection, and then give a rational proper parametrization in the affrmative case. For a rational parametric surface, the authors also determine the developability and give a proper reparametrization for the developable surface.

关键词： Parametrization proper rational developable surface reparametrization

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A dynamic key node learning algorithm in evolving financial network: case study during the period of coronavirus 9th

A dynamic key node learning algorithm in evolving financial ...

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

作者： Lu, Doudou Deng, Wei Xu, Yongyin Shi, Yong College of Big Data Statistics Guizhou University of Finance and Economics Guizhou550025 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Finding an influential set of nodes in the interconnected network, whose activities would maximally influence the development of overall network, is a fundamental topic in finance, biology, social science, epidemic control and other scenarios with respect to network science. In recent years, some works are proposed to find key nodes via network connectivity measures, these studies assume a static environment, and besides, key nodes are calculated through pairwise connectivity, the number of connected components and other measures from the perspective of graph theory. However, these studies leave out of consideration on the dynamic evolving environment and causal relationships in the network. In this paper, we introduce a key node learning algorithm in an evolving financial network. The intuition of the algorithm is to model the causality of financial entities over the time, and the metrics for cascading networks are proposed to grade the entities in the network. In the experiment, the worldwide financial activities before and during the period of coronavirus are analyzed. The proposed metrics can effectively capture the influential entities and quantitatively describe these influences on the whole financial system. © 2022 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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ESL-SELO:A Robust Image Denoising Algorithm with Penalty

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 753-770页

作者： Guo-hua WANG San-guo ZHANG Peng-jie DAI School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing100049China School of Business Remin University of ChinaBeijing 100872China

Robust image recovery methods have been attracted more and more attention in recent decades for its good property of tolerating system errors or measuring noise. In this paper, we propose a new robust method （ESL-SELO） to recover nosing image, which combine exponential loss function and seamless-L0 （SELO） penalty function to guarantee both accuracy and robustness of the estimator. Theoretical result showed that our method has a local optimal solution and good asymptotic properties. Finally, we compare our method with other methods in simulation which shows better robustness and takes much less time.

关键词： denoising algorithm penalty function robust SELO

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Trade credit or credit insurance? A green supply chain finance design scheme with multi-objective programming

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Journal of Intelligent and Fuzzy Systems 2023年第2期45卷 2707-2721页

作者： Zhang, Linzi Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing China University of Birmingham Edgbaston Birmingham United Kingdom 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 Sciences Beijing China

Classical supply chain finance (SCF) primarily focuses on the financial service among all upstream and downstream supply chain participants. Due to the continuously deteriorating of the ecological environment, an environmental-friendly SCF system is urgently needed. In this paper, we propose a novel SCF design scheme with environmental concerns, i.e., green supply chain finance (GSCF), consider the financing channels both from banks and from consumers, and design a bi-objective optimization model that depicts the trade-off between the benefit and the emission. Further, an improved normalized normal constraint (INNC) Pareto method is developed to address the optimal financing strategy of the bi-objective model. We then conduct a numerical case of a Taiwanese steel firm to verify the effectiveness and efficiency of our method. Results show that our model provides a portfolio of optimal solutions on Pareto frontier which can be applied as an effective decision support system when designing a GSCF. Furthermore, the sensitivity analysis also presents the impact of environmental investment cost, technological ratio of companies and the interest rate of trade credit on the optimal configuration of the GSCF. © 2023 - IOS Press. All rights reserved.

关键词： Multiobjective optimization

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Uniform Pricing Strategy vs. Price Differentiation Strategy in the Presence of Cost Saving and Demand Increasing

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Journal of Systems Science & Complexity 2019年第3期32卷 932-946页

作者： CAI Qiang LUO Chunlin TIAN Xin WANG Shouyang 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 Sciences Beijing 100190 China School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

Price differentiation or discrimination strategy has been regarded as the best choice for firms with online and offline channels, however, recent years often witnessed the practices of the uniform pricing strategy. This paper aims to address the question whether the uniform pricing strategy may be better for the manufacturer, when the uniform pricing strategy has a positive impact on increasing the ciistomer demand and reducing the operations cost. The research shows that the uniform pricing strategy can be bet ter than the price differentiation strategy when the cost saving and demand increasing are large enough or the consumers' acceptance of online channel lies in a certain interval. Moreover, the manufac tu rers or brand owners need a tradeoff bet ween the benefit from online channel and the negative impact from the offline channel when they implement the price differentiation strategy. Finally, the authors obtain some managerial insights and implications based on the numerical analyses, which are in line with the phenomena in practice.

关键词： Cost saving demand increasing dual-channel price differentiation strategy uniform pricing strategy

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Literature Review and Practice Comparison of Technology Foresight 8

Literature Review and Practice Comparison of Technology Fore...

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8th International Conference on Information Technology and Quantitative management, ITQM 2020 and 2021

作者： Minghui, Zhao Hanrui, Ye Yao, Peng Lingling, Zhang 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

With continuous breakthrough of key technologies, common technologies, emerging technologies, cross technologies, and subversive technologies, which have brought profound changes to politics, economy, society, culture, and environment. Under above-mentioned background, further exert the guiding role of technology foresight in technology planning formulation, further study the supporting role of technology foresight in emerging technology management, which is important for academic research, enterprise practice and government decision-making. This article can be divided into two parts, the literature review part mainly includes published articles, keyword network, citation network, which describes the historical context, current status, future trends, limitations and improvement directions of related research in detail. The practice comparison part mainly includes targets and experts, methods and processes, technologies and fields, which is also a feasible perspective for results evaluating. © 2021 The Authors. Published by Elsevier B.V.

关键词： Decision making

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A novel self-adaptive density-based clustering algorithm for radar signal sorting

A novel self-adaptive density-based clustering algorithm for...

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IET International Radar Conference 2015

作者： Liu, Ying Cui, Hongyuan School of Computer and Control University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781849199940

Radar signal sorting, which de-interleaves pulse sequences from different radar emitters, is one of the key techniques in Electronic Support Measures (ESM) system of modern warfare. As the electromagnetic environment in modern electronic wars is complex and dense, conventional multi-parameter de-interleaving techniques cannot achieve satisfactory accuracy. Although clustering algorithms have shown promising results in signal sorting, they all have their own weaknesses. In this paper, we propose a novel densitybased clustering algorithm targeting radar signal sorting problem. Our clustering algorithm determines the number of clusters and the density thresholds by the distribution of the data itself rather than depending on user experience as in K-Means or other existing density-based algorithms. Multiple density thresholds may be generated when necessary. We ran our algorithm on a set of simulation radar data. It outperforms DBSCAN-based signal sorting algorithm a lot in terms of accuracy, missing sorting rate and the error sorting in each set of parameters. In addition, it is observed that our algorithm is insensitive to the parameters, ratio factor, uniformity, and quantile. Thus, our proposed self-adaptive density-based clustering algorithm is promising in solving radar signal sorting problem in real electromagnetic environment.

关键词： Sorting

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