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3rd International Conference on Information Technology and Quantitative management (ITQM)

作者： Liu, Qingyuan Guo, Hongbo Wei, Xianhua Univ Chinese Acad Sci Sch Management Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R 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. (C) 2015 Published by Elsevier B.V.

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

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Uncertain Statistical Inference Models With Imprecise Observations

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2018年第2期26卷 409-415页

作者： Yao, Kai Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

The observations of some samples from the population of a probability density function with unknown parameters are usually imprecise due to various reasons. By employing uncertain variables to model these imprecise observations, this paper proposes an interdiscipline called the uncertain statistical inference, which is composed of statistical inference and uncertainty theory. It presents three types of statistic inference problems with imprecise observations that are the point estimation, the hypothesis test, and the interval estimation. Then, it proposes a method of moments and a method of likelihood function (maximum likelihood estimation) for the first problem, and a method of likelihood ratio function for the other two problems.

关键词： Hypothesis test interval estimation point estimation uncertain variable

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Nonparallel Support Vector Ordinal Regression

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IEEE TRANSACTIONS ON CYBERNETICS 2017年第10期47卷 3306-3317页

作者： Wang, Huadong Shi, Yong Niu, Lingfeng Tian, Yingjie Univ Chinese Acad Sci Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Ordinal regression is a supervised learning problem where training samples are labeled by an ordinal scale. The ordering relation and nonmetric property of the label set distinguish it from the multiclass classification and metric regression. To better exploit the inherent structure in the label and benefit from the hidden information in data distribution, we propose a novel ordinal regression model, which is named as non-parallel support vector ordinal regression (NPSVOR) to emphasis the utilization of nonparallel proximal hyperplanes. The new model constructs a hyperplane for each rank such that the patterns of this rank lie in the close proximity while maintaining clear separation with the other ranks. Since the learning of hyperplanes can be carried out independently, NPSVOR can be trained in parallel. Furthermore, we design an efficient solver at the same time for training the hyperplanes in NPSVOR based on the alternating direction method of multipliers. Extensive experimentation demonstrates that NPSVOR yields a large and statistically significant improvement in terms of generalization performance and training speed against nine baselines.

关键词： Alternating direction method of multiplier (ADMM) nonparallel SVM ordinal regression proximal hyper-plane support vector machine (SVM)

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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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Learning With label Proportions via NPSVM

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IEEE TRANSACTIONS ON CYBERNETICS 2017年第10期47卷 3293-3305页

作者： Qi, Zhiquan Wang, Bo Meng, Fan Niu, Lingfeng Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China

Recently, learning from label proportions (LLPs), which seeks generalized instance-level predictors merely based on bag-level label proportions, has attracted widespread interest. However, due to its weak label scenario, LLP usually falls into a transductive learning framework accounting for an intractable combinatorial optimization issue. In this paper, we propose a brand new algorithm, called LLPs via nonparallel support vector machine (LLP-NPSVM), to facilitate this dilemma. To harness satisfactory data adaption, instead of transductive learning fashion, our scheme determined instance labels according to two nonparallel hyper-planes under the supervision of label proportion information. In a geometrical view, our approach can be interpreted as an alternative competitive method benefiting from large margin clustering. In practice, LLP-NPSVM can be efficiently addressed by applying two fast sequential minimal optimization paths iteratively. To rationally support the effectiveness of our method, finite termination and monotonic decrease of the proposed LLP-NPSVM procedure were essentially analyzed. Various experiments demonstrated our algorithm enjoys rapid convergence and robust numerical stability, along with best accuracies among several recently developed methods in most cases.

关键词： k-plane clustering learning with label proportions (LLPs) nonparallel support vector machine (NPSVM)

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A learnable sampling method for scalable graph neural networks

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NEURAL NETWORKS 2023年 162卷 412-424页

作者： Zhao, Weichen Guo, Tiande Yu, Xiaoxi Han, Congying Univ Chinese Acad Sci UCAS Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China MediaTek Singapore Singapore

With the development of graph neural networks, how to handle large-scale graph data has become an increasingly important topic. Currently, most graph neural network models which can be extended to large-scale graphs are based on random sampling methods. However, the sampling process in these models is detached from the forward propagation of neural networks. Moreover, quite a few works design sampling based on statistical estimation methods for graph convolutional networks and the weights of message passing in GCNs nodes are fixed, making these sampling methods not scalable to message passing networks with variable weights, such as graph attention networks. Noting the end-to-end learning capability of neural networks, we propose a learnable sampling method. It solves the problem that random sampling operations cannot calculate gradients and samples nodes with an unfixed probability. In this way, the sampling process is dynamically combined with the forward propagation process of the features, allowing for better training of the networks. And it can be generalized to all message passing models. In addition, we apply the learnable sampling method to GNNs and propose two models. Our method can be flexibly combined with different graph neural network models and achieves excellent accuracy on benchmark datasets with large graphs. Meanwhile, loss function converges to smaller values at a faster rate during training than past methods. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Graph neural networks Large-scale data Learnable sampling method

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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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Latent neighborhood-based heterogeneous graph representation

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NEURAL NETWORKS 2022年第0期154卷 413-424页

作者： Xiao, Yang Quan, Pei Lei, MingLong Niu, Lingfeng Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Graph, as a powerful data structure, has shown superior capability on modeling complex systems. Since real-world objects and their interactions are often multi-modal and multi-typed, compared with traditional homogeneous graphs, heterogeneous graphs can represent real-world objects more effectively. Meanwhile, rich semantic information brings great challenges for learning heterogeneous graph representation (HGR). Most existing HGR methods are based on the concept of meta-path, which is constructed based on direct neighbors and define composite semantic relations in heterogeneous graph. However, when the direct neighbor information is inadequate, which always happens due to insufficient observation, the quality of meta-paths cannot be guaranteed. Therefore, we propose a novel HGR framework based on latent direct neighbors. Specifically, random walks are first utilized to discover the potential candidates from indirect neighbors. Then HodgeRank is introduced to determine the latent direct neighbors according to their importance to the target. After that, neighborhood relationships are augmented with the selected latent direct neighbors, and the adjacency tensor of the heterogeneous graph is refactored correspondingly. Finally, Graph Transformer Network is adopted to construct semantic meta-paths automatically and generate HGR. Numerical experiments on different real-world heterogeneous networks show that our new approach can produce more meta-path instances and introduce more complex and diverse semantic information, and consequently achieves more accurate predictions compared with several state-of-the-art baselines. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Heterogeneous graph Graph neural networks Graph representation learning Meta-path generation HodgeRank

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Who knows more? The role of structural hole spanners in accurate information identification on social media

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Pacific-Basin Finance Journal 2025年

作者： Man Guo Wen Long Yanqiang Zhong Wei Zhang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 PR China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 PR China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 PR China Department of Information Systems and Management Engineering Southern University of Science and Technology Shenzhen 518055 PR China

Based on structural hole theory, this study explores the differences in the ability of Chinese social media users with different characteristics to provide accurate information. Using over 20 million interactive data from 2.19 million users, we construct a social network and identify structural hole spanners and ordinary users. As key players in a network, structural hole spanners are typically located at the intersections of different groups or information sources. They differ from ordinary users in the way they gather and disseminate information. The empirical results indicate that the accuracy of market judgements by structural hole spanners is at least 12.38 % higher than that of ordinary users. Strategy simulations show that using information from structural hole spanners to make investment decisions can achieve a 116 % cumulative return during the backtesting period, nearly four times that of ordinary users. This information advantage occurs primarily because opinion distance and information uniqueness play significant roles in improving information accuracy. This study not only helps deepen understanding of information dissemination characteristics among heterogeneous users on social media but also provides empirical support for the application of structural hole theory in finance, with important theoretical and practical implications.

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Fast Acquisition of Spread Spectrum Signals Using Multiple GPUs

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2019年第6期55卷 3117-3125页

作者： Liu, Ying Cui, Hongyuan Zhao, Renliang Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Spread spectrum (SS) signal acquisition in satellite communication is a very computation intensive technique, which hinders the development of real-time spread spectrum signal acquisition. In this paper, in order to achieve real-time acquisition, we propose a multi-GPU based SS signal acquisition algorithm. First, sliding correlation, the computation kernel, is formulated and efficiently parallelized by CUDA. Second, a CUDA-enabled SS signal acquisition algorithm is implemented by adopting the CUDA-enabled sliding correlation. Third, a multi-GPU based algorithm is implemented by using multi-GPU programming. The performance is evaluated in a real SS telemetry system. Real-time acquisition is achieved in all cases by using a single K40 GPU. Furthermore, an average of 374.7x speedup (max 473x) in 6 datasets is achieved when using four K40 GPUs. Good scalability is observed when varying the parameters.

关键词： Graphics processing units Instruction sets Kernel Correlation Signal processing algorithms Receivers Telemetry Acquisition CUDA multi-GPU parallel computing spread spectrum (SS) communication telemetry

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