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arXiv 2017年

作者： Yang, Qing-Qing Ching, Wai-Ki Gu, Jia-Wen Wong, Tak Kwong Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong Hughes Hall Wollaston Road Cambridge United Kingdom School of Economics and Management Beijing University of Chemical Technology North Third Ring Road Beijing China Department of Mathematics Southern University of Science and Technology Shenzhen China Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong

In this paper, we study optimal liquidation problems in a randomly-Terminated horizon. We consider the liquidation of a large single-Asset portfolio with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact. Three different scenarios are analyzed under Almgren-Chriss's market impact model to explore the relation between opti-mal liquidation strategies and potential inventory risk arising from the uncertainty of the liquidation horizon. For cases where no closed-form solutions can be obtained, we verify comparison principles for viscosity solutions and characterize the value function as the unique viscosity solution of the associated Hamilton-Jacobi-Bellman (HJB) equation. Copyright © 2017, The Authors. All rights reserved.

关键词： Dynamic programming

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On infectious model for dependent defaults

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Risk and Decision Analysis 2017年第4期6卷 249-261页

作者： Ching, Wai-Ki Gu, Jia-Wen Li, Xiaoyue Siu, Tak-Kuen Zheng, Harry Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hughes Hall University of Cambridge Wollaston Road Cambridge United Kingdom School of Economics and Management Beijing University of Chemical Technology North Third Ring Road Beijing China Department of Mathematics Southern University of Science and Technology Shenzhen China Department of Physics National University of Defense Technology Changsha China Department of Applied Finance and Actuarial Studies Faculty of Business and Economics Macquarie University Sydney 2109 NSW Australia Department of Mathematics Imperial College London SW7 2AZ United Kingdom

In this paper, we propose a general framework for modeling discrete-time default risk where default processes for all the entities are governed by predictable interacting default probabilities. We give a general formula for the joint distribution of two important random variables featuring the severity of the crisis: duration of a crisis (T) and severity of the defaults (W T). In particular, we present a two-sector Markovian infectious model, where the default probability is switching over time and depends on the current number of defaults of both sectors. The central idea of this model is that the causality of defaults of two sectors is in both directions, which enrich dynamics of the dependent default risk. The Bayesian Information Criterion (BIC) is adopted to compare the proposed model with the two-sector model in credit literature using real data. Numerical experiments are given to demonstrate that our proposed model is statistically better. © 2017 - IOS Press and the authors. All rights reserved.

关键词： correlated defaults crisis duration Infectious models

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GLOBAL CLIMATE

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Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

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On Eigen-Matrix Translation Method for Classification of Biological Data

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Journal of Systems Science & Complexity 2015年第5期28卷 1212-1230页

作者： JIANG Hao QIU Yushan CHENG Xiaoqing CHING Waiki Department of Mathematics School of Information Renmin University of China Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong

Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines(SVMs)are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigenmatrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.

关键词： Classification dimension reduction eigen-matrix translation glycan data kernel method(KM) support vector machine(SVM)

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On classification of biological data using outlier detection 12

On classification of biological data using outlier detection

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12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management, ISORA 2015

作者： Qiu, Yushan Cheng, Xiaoqing Hou, Wenpin Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong

ISBN: (纸本)9781785610851

With the rapid development of information technology, the number of datasets, as well as their complexity and dimension, have been growing dramatically. This dramatic growth of biology data and non-biological commercial databases becomes a challenging issue in data mining. Classification technique is one of the major tools in the captured research area. However, the performance of classification may be degraded when there exists noise in the captured databases. Therefore, outlier detection becomes an urgent need and the issue of how to integrate outlier detection method and classification techniques is an important and challenging issue. In this paper, we proposed a novel and effective approach based on k-means clustering to identify outliers in the databases. In particular, we employed one of famous classification techniques, Support Vector Machine (SVM), owing to its ability to handle highdimensional data set. We also compare the classification results with the multivariate outlier detection method. Numerical results on two different data sets indicate that the classification results after removing the outliers by our proposed method are much better than the multivariate outlier detection method. © 2015 IET.

关键词： Classification (of information)

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On Using Physico-Chemical Properties of Amino Acids in String Kernels for Protein Classification via Support Vector Machines

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Journal of Systems Science & Complexity 2015年第2期28卷 504-516页

作者： LI Limin AOKI-KINOSHITA Kiyoko F CHING Wai-Ki JIANG Hao Institute of Information and System Science Xi'an Jiaotong University Department of Bioinformatics Faculty of EngineeringSoka University Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Department of Mathematics School of InformationRenmin University of China

String kernels are popular tools for analyzing protein sequence data and they have been successfully applied to many computational biology problems. The traditional string kernels assume that different substrings are independent. However, substrings can be highly correlated due to their substructure relationship or common physico-chemical properties. This paper proposes two kinds of weighted spectrum kernels： The correlation spectrum kernel and the AA spectrum kernel. We evMuate their performances by predicting glycan-binding proteins of 12 glycans. The results show that the correlation spectrum kernel and the AA spectrum kernel perform significantly better than the spectrum kernel for nearly all the 12 glycans. By comparing the predictive power of AA spectrum kernels constructed by different physico-chemical properties, the authors can also identify the physico- chemical properties which contributes the most to the glycan-protein binding. The results indicate that physico-chemical properties of amino acids in proteins play an important role in the mechanism of glycamprotein binding.

关键词： AAindex AA spectrum kernel correlation spectrum kernel physico-chemical properties string kernel weighted spectrum kernel.

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On Eigen-Matrix Translation Method for Classification of Biological Data

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Journal of Systems Engineering and Electronics 2015年第5期26卷 1212-1230页

Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.

关键词： Classification dimension reduction eigen-matrix translation glycan data kernel method（KM） support vector machine （SVM）

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A multiple linear regression model for structure of n-linked oligosaccharides 12

A multiple linear regression model for structure of n-linked...

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12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management, ISORA 2015

作者： Cheng, Xiaoqing Ching, Wai-Ki Hou, Wenpin Aoki-Kinoshita, Kiyoko F. Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Department of Engineering Soka University Tokyo Japan

ISBN: (纸本)9781785610851

It is well-known that carbohydrate sugar chains, or glycans, play various well in cellular processes, including cancer, but the elucidation of glycans is difficult because of their complex structure. Both computational methods and mathematical models are necessary to integrate and analyze the information of glycomics data so as to efficiently detect glycan structures. In this paper, we propose a new model to predict the structure of N-glycans, which are the most common type of glycans. Our proposed prediction method is based on a Multiple Linear Regression (MLR) model. The coefficients of our proposed model are solved by using experimental data. We obtain our data from High Performance Liquid Chromatography (HPLC) experiments. Three sources of our data are adopted and they are divided into two parts: elution value on an Amide column and elution value on an OctaDecylSilane (ODS) column. After pre-processing the data, we then construct our proposed MLR model. The obtained correlation coefficients are 0.9680 for the Amide data and 0.9263 for the ODS data. We have also tested the correctness of the model statistically. The model test and correlation coefficients demonstrate both the accuracy and efficiency of our proposed model. © 2015 IET.

关键词： Multiple linear regression

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Projection method for support vector machines with indefinite kernels 12

Projection method for support vector machines with indefinit...

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12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management, ISORA 2015

作者： Jiang, Hao Ching, Wai-Ki Qiu, Yushan Cheng, Xiaoqing Department of Mathematics School of Information Renmin University of China No.59 Zhong Guan Cun Street Hai Dian District Beijing100872 China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong

ISBN: (纸本)9781785610851

In this paper, we tackle with indefinite kernels by introducing projection matrix to formulate a positive semidefinite kernel. The projection matrix has a nice property of sharing the same set of eigenvectors with the original kernel. The proposed model can be regarded as a generalized version of spectrum method(denoising method and flipping method) by varying parameter λ. The problem of selecting optimal λ for optimizing the prediction performance is also considered. Using the Bregman matrix divergence theory, one can realize kernel learning by using unconstrained optimization. And our suggested λ in projection matrix helps to exhibit optimal performance for different values of λ. © 2015 IET.

关键词： Support vector machines

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A semi-tensor product approach for Probabilistic Boolean Networks 8

A semi-tensor product approach for Probabilistic Boolean Net...

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8th International Conference on Systems Biology, ISB 2014

作者： Cheng, Xiaoqing Qiu, Yushan Hou, Wenpin Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781479972944

modeling genetic regulatory networks is an important issue in systems biology. Various models and mathematical formalisms have been proposed in the literature to solve the capture problem. The main purpose in this paper is to show that the transition matrix generated under semi-tensor product approach (Here we call it the probability structure matrix for simplicity) and the traditional approach (Transition probability matrix) are similar to each other. And we shall discuss three important problems in Probabilistic Boolean Networks (PBNs): the dynamic of a PBN, the steady-state probability distribution and the inverse problem. Numerical examples are given to show the validity of our theory. We shall give a brief introduction to semi-tensor and its application. After that we shall focus on the main results: to show the similarity of these two matrices. Since the semi-tensor approach gives a new way for interpreting a BN and therefore a PBN, we expect that advanced algorithms can be developed if one can describe the PBN through semi-tensor product approach. © 2014 IEEE.

关键词： Matrix algebra

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