检索结果-内蒙古大学图书馆

2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012

作者： Song, Na Ching, Wai-Ki Siu, Tak-Kuen Yiu, Cedric Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Hong Kong Department of Applied Finance and Actuarial Studies Center for Financial Risk Macquarie University Sydney NSW 2109 Australia Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)9780769546902

We consider an optimal selection problem for bid and ask quotes subject to a value-at-Risk (VaR) constraint when arrivals of the buy and sell orders are governed by a Poisson process. The problem is formulated as a constrained utility maximization problem over a finite time horizon. Using a diffusion approximation to Poisson arrivals of market orders, the dynamic programming principle can be applied here. We propose an efficient procedure to solve this constrained utility maximization problem based on a successive approximation algorithm. Numerical examples with and without the VaR constraint are used to illustrate the effect of the risk constraint on the dealer's choices. We also conduct numerical experiments to analyze the impacts of the risk constraint on dealer's terminal profit. © 2012 IEEE.

关键词： Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

The role of Eigen-matrix translation in classification of biological datasets

The role of Eigen-matrix translation in classification of bi...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hao Jiang Wai-Ki Ching Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong China

Driven by the challenge of integrating large amount of experimental data obtained from biological research, computational biology and bioinformatics are growing rapidly. Machine learning methods, especially kernel methods with Support Vector Machines (SVMs) are very popular 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 owns a lot of nice properties while the nature of which needs further exploration. We propose that its importance lies in the dimension reduction of predictor attributes within the data set. This can therefore serve as a novel perspective for future research in dimension reduction problems.

关键词： Kernel Proteins Protein engineering Support vector machines Accuracy Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Metabolite biomarker discovery for metabolic diseases by flux analysis

Metabolite biomarker discovery for metabolic diseases by flu...

引用

2012 IEEE 6th International Conference on Systems Biology, ISB 2012

作者： Li, Limin Jiang, Hao Ching, Wai-Ki Vassiliadis, Vassilis S. Institute of Information and System Science Xi'an Jiaotong University Xi'an 710049 China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong Department of Chemical Engineering and Biotechnology University of Cambridge Pembroke Street Cambridge United Kingdom

ISBN: (纸本)9781467343985

Metabolites can serve as biomarkers and their identification has significant importance in the study of biochemical reaction and signalling networks. Incorporating metabolic and gene expression data to reveal biochemical networks is a considerable challenge, which attracts a lot of attention in recent research. In this paper, we propose a promising approach to identify metabolic biomarkers through integrating available biomedical data and disease-specific gene expression data. A Linear Programming (LP) based method is then utilized to determine flux variability intervals, therefore enabling the analysis of significant metabolic reactions. A statistical approach is also presented to uncover these metabolites. The identified metabolites are then verified by comparing with the results in the existing literature. The proposed approach here can also be applied to the discovery of potential novel biomarkers. © 2012 IEEE.

关键词： Metabolites

来源：评论

学校读者我要写书评

暂无评论

Optimal Submission Problem in a Limit Order Book with VaR Constraints

Optimal Submission Problem in a Limit Order Book with VaR Co...

引用

International Joint Conference on Computational Sciences and Optimization, CSO

作者： Na Song Wai-Ki Ching Tak-Kuen Siu Cedric Yiu The Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong China Department of Applied Finance and Actuarial Studies and Center for Financial Risk Faculty of Business and Economics Macquarie University Sydney NSW Australia Department of Applied Mathematics Hong Kong Polytechnic University Hung Hom Hong Kong China

关键词： Reactive power Approximation methods Mathematical model Equations Numerical models Approximation algorithms Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Metabolite biomarker discovery for metabolic diseases by flux analysis

Metabolite biomarker discovery for metabolic diseases by flu...

引用

IEEE International Conference on Systems Biology

作者： Limin Li Hao Jiang Wai-Ki Ching Vassilis S. Vassiliadis Institute of Information and System Science Xi'an Jiaotong University Xi'an China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong China Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK

关键词： Biochemistry Diabetes Humans Obesity Gene expression Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Erratum: Search for new phenomena in final states with large jet multiplicities and missing transverse momentum at = 8 TeV proton-proton collisions using the ATLAS experiment

引用

Journal of High Energy Physics 2014年第1期2014卷 1-18页

作者： G. Aad T. Abajyan B. Abbott J. Abdallah S. Abdel Khalek O. Abdinov R. Aben B. Abi M. Abolins O. S. AbouZeid H. Abramowicz H. Abreu Y. Abulaiti B. S. Acharya L. Adamczyk D. L. Adams T. N. Addy J. Adelman S. Adomeit T. Adye S. Aefsky T. Agatonovic-Jovin J. A. Aguilar-Saavedra M. Agustoni S. P. Ahlen A. Ahmad M. Ahsan G. Aielli T. P. A. Åkesson G. Akimoto A. V. Akimov M. A. Alam J. Albert S. Albrand M. J. Alconada Verzini M. Aleksa I. N. Aleksandrov F. Alessandria C. Alexa G. Alexander G. Alexandre T. Alexopoulos M. Alhroob M. Aliev G. Alimonti L. Alio J. Alison B. M. M. Allbrooke L. J. Allison P. P. Allport S. E. Allwood-Spiers J. Almond A. Aloisio R. Alon A. Alonso F. Alonso A. Altheimer B. Alvarez Gonzalez M. G. Alviggi K. Amako Y. Amaral Coutinho C. Amelung V. V. Ammosov S. P. Amor Dos Santos A. Amorim S. Amoroso N. Amram C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders G. Anders K. J. Anderson A. Andreazza V. Andrei X. S. Anduaga S. Angelidakis P. Anger A. Angerami F. Anghinolfi A. V. Anisenkov N. Anjos A. Annovi A. Antonaki M. Antonelli A. Antonov J. Antos F. Anulli M. Aoki L. Aperio Bella R. Apolle G. Arabidze I. Aracena Y. Arai A. T. H. Arce S. Arfaoui J.-F. Arguin S. Argyropoulos E. Arik M. Arik A. J. Armbruster O. Arnaez V. Arnal O. Arslan A. Artamonov G. Artoni D. Arutinov S. Asai N. Asbah S. Ask B. Åsman L. Asquith K. Assamagan R. Astalos A. Astbury M. Atkinson N. B. Atlay B. Auerbach E. Auge K. Augsten M. Aurousseau G. Avolio D. Axen G. Azuelos Y. Azuma M. A. Baak C. Bacci A. M. Bach H. Bachacou K. Bachas M. Backes M. Backhaus J. Backus Mayes E. Badescu P. Bagiacchi P. Bagnaia Y. Bai D. C. Bailey T. Bain J. T. Baines O. K. Baker S. Baker P. Balek F. Balli E. Banas Sw. Banerjee D. Banfi A. Bangert V. Bansal H. S. Bansil L. Barak S. P. Baranov T. Barber E. L. Barberio D. Barberis M. Barbero D. Y. Bardin T. Barillari M. Barisonzi T. Barklow N. Barlow B. M. Barnett R. M. Barnett A. Baroncelli G. Barone A. J. Barr F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton V. Bartsch A. B Fakultät für Mathematik und Physik Albert-Ludwigs-Universität Freiburg Germany Physikalisches Institut University of Bonn Bonn Germany Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Institut de Física d’Altes Energies and Departament de Física de la Universitat Autònoma de Barcelona Barcelona Spain LAL Université Paris-Sud and CNRS/IN2P3 Orsay France Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam Netherlands Department of Physics Oklahoma State University Stillwater United States of America Department of Physics and Astronomy Michigan State University East Lansing United States of America Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel DSM/IRFU (Institut de Recherches sur les Lois Fondamentales de l’Univers) CEA Saclay (Commissariat à l’Energie Atomique et aux Energies Alternatives) Gif-sur-Yvette France Department of Physics Stockholm University Stockholm Sweden INFN Gruppo Collegato di Udine Udine Italy Department of Physics King’s College London London United Kingdom AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Physics Department Brookhaven National Laboratory Upton United States of America Department of Physics Hampton University Hampton United States of America Department of Physics Yale University New Haven United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Department of Physics Brandeis University Waltham United States of America Vinca Institute of Nuclear Sciences University of Belgrade Belgrade Serbia Laboratorio de Instrumentacao e Fisica Experimental de Particulas - LIP Lisboa Portug

来源：评论

学校读者我要写书评

暂无评论

A modified Newton's method for inverse problem of Probabilistic Boolean Networks with gene perturbations

A modified Newton's method for inverse problem of Probabilis...

引用

5th IEEE International Conference on Systems Biology, ISB 2011

作者： Li, Wen Ching, Wai-Ki Cui, Lu-Bin School of Mathematical Sciences South China Normal University Guangzhou 510631 China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong

ISBN: (纸本)9781457716669

modeling genetic regulatory networks is an important research issue in systems biology. Many mathematical models have been proposed, and among these models, Boolean Network (BN) and its extension Probabilistic Boolean Network (PBN) are popular. In this paper we consider the problem constructing PBNs with gene perturbations. We propose a modified Newton's method to get the gene perturbation probability of the captured problem. Numerical experiments are given to demonstrate both effectiveness and efficiency of our proposed method. © 2011 IEEE.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

On infectious models for dependent default risk

On infectious models for dependent default risk

引用

International Workshop on Computational Transportation Science

作者： Gu, Jiawen Ching, Wai-Ki Siu, Tak-Kuen Department of Mathematics Advanced Modeling and Applied Computing Laboratory University of Hong Kong Pokfulam Road Hong Kong Department of Actuarial Studies and Center for Financial Risk Faculty of Business and Economics Macquarie University Sydney NSW 2109 Australia

ISBN: (纸本)9780769543352

modeling dependent defaults is a key issue in risk measurement and management. In this paper, we introduce a Markovian infectious model to describe the dependent relationship of default processes of credit entities. The key idea of the proposed model is based on the concept of common shocks adopted in the insurance industry. We compare the proposed model to both one-sector and two-sector models considered in the credit literature using real default data. A log-likelihood ratio test is applied to compare the goodness-of- fit of the proposed model. Our empirical results reveal that the proposed model outperforms both the one-sector and two-sector models. © 2011 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

An efficient method of computing impact degrees for multiple reactions in metabolic networks with cycles

An efficient method of computing impact degrees for multiple...

引用

作者： Tamura, Takeyuki Cong, Yang Akutsu, Tatsuya Ching, Wai-Ki Bioinformatics Center Institute for Chemical Research Kyoto University Uji-shi 611-0011 Japan Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong

The impact degree is a measure of the robustness of a metabolic network against deletion of single or multiple reaction(s). Although such a measure is useful for mining important enzymes/genes, it was defined only for networks without cycles. In this paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm to calculate the impact degree. Furthermore we improve this algorithm to reduce computation time for the impact degree by deletions of multiple reactions. We applied our method to the metabolic network of E. coli, that includes reference pathways, consisting of 3281 reaction nodes and 2444 compound nodes, downloaded from KEGG database, and calculate the distribution of the impact degree. The results of our computational experiments show that the improved algorithm is 18.4 times faster than the simple algorithm for deletion of reaction-pairs and 11.4 times faster for deletion of reaction-triplets. We also enumerate genes with high impact degrees for single and multiple reaction deletions. © 2011 The Institute of Electronics, Information and Communication Engineers.

关键词： Metabolism

来源：评论

学校读者我要写书评

暂无评论

On Infectious Models for Dependent Default Risk

On Infectious Models for Dependent Default Risk

引用

The Fourth International Joint Conference on Computational Science and Optimization(第四届计算科学与优化国际大会 CSO 2011)

作者： Jiawen Gu Wai-Ki Ching Tak-Kuen Siu Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Department of Actuarial Studies and Center for Financial Risk Faculty of Business and Economics Macq

modeling dependent defaults is a key issue in risk measurement and management. In this paper, we introduce a Markovian infectious model to describe the dependent relationship of default processes of credit entities. The key idea of the proposed model is based on the concept of common shocks adopted in the insurance industry. We compare the proposed model to both one-sector and two-sector models considered in the credit literature using real default data. A log-likelihood ratio test is applied to compare the goodness-of-fit of the proposed model. Our empirical results reveal that the proposed model outperforms both the one-sector and two-sector models.

关键词： Markov chains one-sector model two-sector model chain reaction of infectious defaults default risk common shock

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：