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Journal of Computer Science & Technology 2010年第3期25卷 444-457页

作者：常利军于旭秦璐 department of systems engineering and engineering management The Chinese University of Hong Kong

Ranking is a main research issue in IR-styled keyword search over a set of documents. In this paper, we study a new keyword search problem, called context-sensitive document ranking, which is to rank documents with an additional context that provides additional information about the application domain where the documents are to be searched and ranked. The work is motivated by the fact that additional information associated with the documents can possibly assist users to find more relevant documents when they are unable to find the needed documents from the documents alone. In this paper, a context is a multi-attribute graph, which can represent any information maintained in a relational database, where multi-attribute nodes represent tuples, and edges represent primary key and foreign key references among nodes. The context-sensitive ranking is related to several research issues, how to score documents, how to evaluate the additional information obtained in the context that may contribute to the document ranking, how to rank the documents by combining the scores/costs from the documents and the context. More importantly, the relationships between documents and the information stored in a relational database may be uncertain, because they are from different data sources and the relationships are determined systematically using similarity match which causes uncertainty. In this paper, we concentrate ourselves on these research issues, and provide our solution on how to rank the documents in a context where there exist uncertainty between the documents and the context. We confirm the effectiveness of our approaches by conducting extensive experimental studies using real datasets. We present our findings in this paper.

关键词： document ranking uncertain ranking structure cost similarity

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Handling query skew in large indexes： a view based approach

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Frontiers of Computer Science 2018年第1期12卷 146-162页

作者： Weihuang HUANG Jeffrey Xu YU Zechao SHANG systems engineering and engineering management The Chinese University of Hong Kong Hong Kong China

Indexing is one of the most important tech- niques to facilitate query processing over a multi-dimensional dataset. A commonly used strategy for such indexing is to keep the tree-structured index balanced. This strategy reduces query processing cost in the worst case, and can handle all different queries equally well. In other words, this strategy implies that all queries are uniformly issued, which is par- tially because the query distribution is not possibly known and will change over time in practice. A key issue we study in this work is whether it is the best to fully rely on a bal- anced tree-structured index in particular when datasets be- come larger and larger in the big data era. This means that, when a dataset becomes very large, it becomes unreasonable to assume that all data in any subspace are equally important and are uniformly accessed by all queries at the index level. Given the existence of query skew and the possible changes of query skew, in this paper, we study how to handle such query skew and such query skew changes at the index level without sacrifice of supporting any possible queries in a well- balanced tree index and without a high overhead. To tackle the issue, we propose index-view at the index level, where an index-view is a short-cut in a balanced tree-structured index to access objects in the subspaces that are more frequently accessed, and propose a new index-view-centric framework for query processing using index-views in a bottom-up man- ner. We study index-views selection problem in both static and dynamic setting, and we confirm the effectiveness of our approach using large real and synthetic datasets.

关键词： multi-dimensional index query adaptive index-view

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A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning

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Machine Intelligence Research 2023年第3期20卷 318-334页

作者： Wai-Chung Kwan hong-Ru Wang Hui-Min Wang Kam-Fai Wong The systems engineering and engineering management department The Chinese University of Hong KongHong Kong 999077China

Dialogue policy learning(DPL)is a key component in a task-oriented dialogue(TOD)*** goal is to decide the next action of the dialogue system,given the dialogue state at each turn based on a learned dialogue *** learning(RL)is widely used to optimize this dialogue *** the learning process,the user is regarded as the environment and the system as the *** this paper,we present an overview of the recent advances and challenges in dialogue policy from the perspective of *** specifically,we identify the problems and summarize corresponding solutions for RL-based dialogue policy *** addition,we provide a comprehensive survey of applying RL to DPL by categorizing recent methods into five basic elements in *** believe this survey can shed light on future research in DPL.

关键词： Dialogue policy learning(DPL) task-oriented dialogue system(TOD) reinforcement learning(RL) dialogue system Markov decision process

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Applications of gauge duality in robust principal component analysis and semidefinite programming

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Science China Mathematics 2016年第8期59卷 1579-1592页

作者： MA ShiQian YANG JunFeng department of systems engineering and engineering management The Chinese University of Hong Kong department of Mathematics Nanjing University

Gauge duality theory was originated by Preund （1987）, and was recently further investigated by Friedlander et al. （2014）. When solving some matrix optimization problems via gauge dual, one is usually able to avoid full matrix decompositions such as singular value and/or eigenvalue decompositions. In such an approach, a gauge dual problem is solved in the first stage, and then an optimal solution to the primal problem can be recovered from the dual optimal solution obtained in the first stage. Recently, this theory has been applied to a class of semidefinite programming （SDP） problems with promising numerical results by Friedlander and Mac^to （2016）. We establish some theoretical results on applying the gauge duality theory to robust principal component analysis （PCA） and general SDP. For each problem, we present its gauge dual problem, characterize the optimality conditions for the primal-dual gauge pair, and validate a way to recover a primal optimal solution from a dual one. These results are extensions of Friedlander and Macedo （2016） from nuclear norm regularization to robust PCA and from a special class of SDP which requires the coefficient matrix in the linear objective to be positive definite to SDP problems without this restriction. Our results provide further understanding in the potential advantages and disadvantages of the gauge duality theory.

关键词： gauge optimization gauge duality polar function antipolar set singular value decomposition robust principal component analysis semidefinite programming

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DYNAMIC RESOURCE ALLOCATION FOR EFFICIENT PATIENT SCHEDULING： A DATA-DRIVEN APPROACH

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Journal of systems Science and systems engineering 2017年第4期26卷 448-462页

作者： Monique Bakker Kwok-Leung Tsui department of systems engineering and engineering management City University of Hong Kong China

Efficient staff rostering and patient scheduling to meet outpatient demand is a very complex and dynamic task. Due to fluctuations in demand and specialist availability, specialist allocation must be very flexible and non-myopic. Medical specialists are typically restricted in sub-specialization, serve several patient groups and are the key resource in a chain of patient visits to the clinic and operating room （OR）. To overcome a myopic view of once-off appointment scheduling, we address the patient flow through a chain of patient appointments when allocating key resources to different patient groups. We present a new, data-driven algorithmic approach to automatic allocation of specialists to roster activities and patient groups. By their very nature, simplified mathematical models cannot capture the complexity that is characteristic to the system being modeled. In our approach, the allocation of specialists to their day-to-day activities is flexible and responsive to past and present key resource availability, as well as to past resource allocation. Variability in roster activities is actively minimized, in order to enhance the supply chain flow. With discrete-event simulation of the application case using empirical data, we illustrate how our approach improves patient Service Level （SL, percentage of patients served on-time） as well as Wait Time （days）, without change in resource capacity.

关键词： Patient scheduling dynamic rostering patient care path discrete-event simulation

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Alternating Direction Method of Multipliers for Sparse Principal Component Analysis

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Journal of the Operations Research Society of China 2013年第2期1卷 253-274页

作者： Shiqian Ma department of systems engineering and engineering management The Chinese University of Hong KongShatinNTHong Kong

We consider a convex relaxation of sparse principal component analysisproposed by d' Aspremont et al. (SIAM Rev. 49:434 448, 2007). This convex relax-ation is a nonsmooth semidefinite programming problem in which the ξ1 norm of thedesired matrix is imposed in either the objective function or the constraint to improvethe sparsity of the resulting matrix. The sparse principal component is obtained by arank- one decomposition of the resulting sparse matrix. We propose an alternating di-rection method based on a variable-splitting technique and an augmented I agrangianframework for solving this nonsmooth semidefinite programming problem. In con-trast to the first-order method proposed in d' Aspremont et al. (SIAM Rev. 49:434448, 2007), which solves approximately the dual problem of the original semidefiniteprogramming problem, our method deals with the primal problem directly and solvesit exactly, which guarantees that the resulting matrix is a sparse matrix. A globalconvergence result is established for the proposed method. Numerical results on bothsynthetic problems and the real applications from classification of text data and senatevoting data are reported to demonstrate the efficacy of our method.

关键词： Sparse PCA Semidefinite programming Alternating direction method Augmented L agrangian method Deflation Projection onto the simplex

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OPTIMAL PURCHASING PROCESS FOR ELECTRICITY AND RENEWABLE ENERGY CREDITS WITH PRICE AND DEMAND UNCERTAINTY

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Journal of systems Science and systems engineering 2012年第2期21卷 184-203页

作者： Zhiyuan CHEN Yiwen SU Houmin YAN department of Automation Tsinghua University Beijing 100084 China department of systems engineering and engineering management The Chinese University of Hong Kong department of management Sciences City University of Hong Kong Kowloon China

This paper optimizes the electricity and renewable energy credit （REC） purchasing process for energy distribution. Electricity is traded in deregulated time-sequential markets at fluctuating prices. Optimal electricity purchasing under price and demand uncertainty is a challenging task for electricity distributors, and the recently implemented renewable portfolio standards （RPS） further complicate the purchasing process. Government regulatory decisions concerning the RPS require distributors to purchase corresponding certificates, namely RECs, equivalent to a certain percentage of their electricity sales. This paper formulates and optimizes the joint purchasing process for electricity and RECs. It also analyzes the effect of RPS policy on electricity distributors.

关键词： Renewable energy credit information update dynamic programming

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A CASE STUDY FOR ADVANCED PLANNING AND SCHEDULING (APS)

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Journal of systems Science and systems engineering 2011年第4期20卷 460-474页

作者： Kejia CHEN Ping Jl Qing WANG School of management Fuzhou University Fuzhou 350108 China department of Industrial and systems engineering The Hong Kong Polytechnic University Hong Kong department of systems engineering Northeastern University Shenyang 110819 China

This paper presents a case study for the advanced planning and scheduling （APS） problem encountered in a light source manufacturer. The APS problem explicitly considers due dates of products, operation sequences among items, and capacity constraints of the manufacturing system. The objective of the problem is to seek the minimum cost of both production idle time and tardiness or earliness penalty of an order. An intelligent heuristic is applied to the problem, and the results demonstrate that significant production performances can be achieved while ensuring customer satisfaction as opposed to normal oractices followed in the company relying on human expertise.

关键词： Advanced planning and scheduling case study intelligent heuristic

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On the Sublinear Convergence Rate of Multi-block ADMM

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Journal of the Operations Research Society of China 2015年第3期3卷 251-274页

作者： Tian-Yi Lin Shi-Qian Ma Shu-Zhong Zhang department of systems engineering and engineering management The Chinese University of Hong KongShatinNew TerritoriesHong KongChina department of Industrial and systems engineering University of MinnesotaMinneapolisMN 55455USA

The alternating direction method of multipliers(ADMM)is widely used in solving structured convex optimization *** its success in practice,the convergence of the standard ADMM for minimizing the sum of N(N≥3)convex functions,whose variables are linked by linear constraints,has remained unclear for a very long ***,Chen et al.(Math Program,doi:10.1007/s10107-014-0826-5,2014)provided a counter-example showing that the ADMM for N≥3 may fail to converge without further *** the ADMM for N≥3 has been very successful when applied to many problems arising from real practice,it is worth further investigating under what kind of sufficient conditions it can be guaranteed to *** this paper,we present such sufficient conditions that can guarantee the sublinear convergence rate for the ADMM for N≥***,we show that if one of the functions is convex(not necessarily strongly convex)and the other N-1 functions are strongly convex,and the penalty parameter lies in a certain region,the ADMM converges with rate O(1/t)in a certain ergodic sense and o(1/t)in a certain non-ergodic sense,where t denotes the number of *** a by-product,we also provide a simple proof for the O(1/t)convergence rate of two-blockADMMin terms of both objective error and constraint violation,without assuming any condition on the penalty parameter and strong convexity on the functions.

关键词： Alternating direction method of multipliers Sublinear convergence rate Convex optimization

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Cooperative Target Fencing of Multiple Vehicles for a General Target with Connectivity Preservation and Collision Avoidance

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Journal of systems Science & Complexity 2024年第1期37卷 136-151页

作者： PAN Zini CHEN Ben M department of Mechanical and Automation engineering The Chinese University of Hong KongHong KongChina

In this paper,the authors study the cooperative target-fencing problem for n-dimensional systems and a target with a general *** using the velocity of the vehicles,a position feedback control law is proposed to fence the general target into the convex hull formed by the ***,the dynamics of each vehicle is described by a double-integrator *** potential functions are designed to guarantee connectivity preservation of the communication network and collision avoidance among the *** proposed approach can deal with a target whose trajectory is any twice continuously differentiable function of *** effectiveness of the result is verified by a numerical example.

关键词： Multi-agent systems networked systems target fencing

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