Molecular distance geometry methods: from continuous to discrete

Distance geometry problems (DGP) arise from the need to position entities in the Euclidean K‐space given some of their respective distances. Entities may be atoms (molecular distance geometry), wireless sensors (sensor network localization), or abstract vertices of a graph (graph drawing). In the context of molecular distance geometry, the distances are usually known because of chemical properties and nuclear magnetic resonance experiments; sensor networks can estimate their relative distance by recording the power loss during a two‐way exchange; finally, when drawing graphs in two or three dimensions, the graph to be drawn is given, and therefore distances between vertices can be computed. DGPs involve a search in a continuous Euclidean space, but sometimes the problem structure helps reduce the search to a discrete set of points. In this paper we survey some continuous and discrete methods for solving some problems of molecular distance geometry.     

Solving diameter‐constrained minimum spanning tree problems by constraint programming

The diameter‐constrained minimum spanning tree problem consists in finding a minimum spanning tree of a given graph, subject to the constraint that the maximum number of edges between any two vertices in the tree is bounded from above by a given constant. This problem typically models network design applications where all vertices communicate with each other at a minimum cost, subject to a given quality requirement. We propose alternative formulations using constraint programming that circumvent weak lower bounds yielded by most mixed‐integer programming formulations. Computational results show that the proposed formulation, combined with an appropriate search procedure, solves larger instances and is faster than other approaches in the literature.     

求解资源受限项目调度问题的约束规划/数学规划混合算法

利用约束规划（constraintprogramming,CP）与数学规划（mathematicalprogramming,MP）结合的方法求解调度问题已经获得了一些较好的研究成果,正成为调度问题研究领域的一个新的热点研究方向．本文针对求解资源受限项目调度问题（RCPSP）的整数规划模型,设计了基于CP技术的问题和模型预处理方法,证明了整数规划模型的有效不等式定理,提出了通过将项目子网络图转化为加权最大团问题求解后获得有效不等式的方法．引用标准问题库PSPLIB中的一组典型问题进行求解实验,结果表明本文提出的有效不等式可以明显改进模型的求解质量和时间性能．论文最后对实验结果进行了深入讨论,讨论了未来的研究方向．     

Parallel machine scheduling with flexible resources

Parallel machine flexible resource scheduling (PMFRS) problems consider an additional flexible resource (e.g. operators), which can be freely allocated to any jobs and/or any machines and may speed-up the process in proportion to its amount. If job–machine assignment is unspecified, the problem is referred to as unspecified PMFRS (UPMFRS). This paper reviews the mathematical models of both PMFRS and UPMFRS problems in the literature and not only gives some extensions to the model of dynamic PMFRS problem but also presents integer programming (IP) models for static and dynamic UPMFRS problems with the objective of minimizing makespan. To solve large-sized dynamic PMFRS and UPMFRS problems, a relaxed IP based constraint programming (CP) approach is also proposed. All IP models and the proposed IP/CP approach are tested with an extensive computational study. The results of the computational experiments are discussed with respect to the major parameters of the problem and conclusions are drawn.     

Solving the bi‐objective capacitated p‐median problem with multilevel capacities using compromise programming and VNS

A bi‐objective optimisation using a compromise programming (CP) approach is proposed for the capacitated p‐median problem (CPMP) in the presence of the fixed cost of opening facility and several possible capacities that can be used by potential facilities. As the sum of distances between customers and their facilities and the total fixed cost for opening facilities are important aspects, the model is proposed to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal location of open facilities with their optimal capacity. Two approaches are designed to deal with the bi‐objective CPMP, namely CP with an exact method and with a variable neighbourhood search (VNS) based matheuristic. New sets of generated instances are used to evaluate the performance of the proposed approaches. The computational experiments show that the proposed approaches produce interesting results.     

A Two-Stage Decomposition of High School Timetabling applied to cases in Denmark

Integer Programming (IP) has been used to model educational timetabling problems since the very early days of Operations Research. It is well recognized that these IP models in general are hard to solve, and this area of research is dominated by heuristic solution approaches. In this paper a Two-Stage Decomposition of an IP model for a practical case of high school timetabling is shown. This particular timetabling problem consists of assigning lectures to both a timeslot and a classroom, which is modeled using a very large amount of binary variables. The decomposition splits this model into two separate problems (Stage I and Stage II) with far less variables. These two separate problems are solved in sequence, such that the solution for the Stage I model is given as input to the Stage II model, implying that irreversible decisions are made in Stage I. However, the objective of the Stage II model is partly incorporated in the Stage I model by exploiting that Stage II can be seen as a minimum weight maximum matching problem in a bipartite graph. This theoretically strengthens the decomposition in terms of global optimality. The approach relies on Hall's theorem for the existence of matchings in bipartite graphs, which in its basic form yields an exponential amount of constraints in the Stage I model. However, it is shown that only a small subset of these constraints is needed, making the decomposition tractable in practice for IP solvers. To evaluate the decomposition, 100 real-life problem instances from the database of the high school ERP system Lectio are used. Computational results show that the decomposition performs significantly better than solving the original IP, in terms of both found solutions and bounds.     

Improving vector space embedding of graphs through feature selection algorithms

Graph based pattern representation offers a versatile alternative to vectorial data structures. Therefore, a growing interest in graphs can be observed in various fields. However, a serious limitation in the use of graphs is the lack of elementary mathematical operations in the graph domain, actually required in many pattern recognition algorithms. In order to overcome this limitation, the present paper proposes an embedding of a given graph population in a vector space Rⁿ. The key idea of this embedding approach is to interpret the distances of a graph g to a number of prototype graphs as numerical features of g. In previous works, the prototypes were selected beforehand with heuristic selection algorithms. In the present paper we take a more fundamental approach and regard the problem of prototype selection as a feature selection or dimensionality reduction problem, for which many methods are available. With several experiments we show the feasibility of graph embedding based on prototypes obtained from such feature selection algorithms and demonstrate their potential to outperform previous approaches.     

LP bounds in various constraint programming approaches for orthogonal packing

We consider the 2D orthogonal feasibility problem (OPP-2) and the 2D strip packing problem (SPP-2). Given a set of rectangular items, the OPP-2 is to decide whether all items can be orthogonally packed into the given rectangular container; the SPP-2 is to find a packing of all items occupying the minimal height of the given semi-infinite strip. Both OPP-2 and SPP-2 are considered without items rotation. We investigate the known Constraint Programming (CP) approaches for OPP-2, in particular the dichotomy and disjunctive branching strategies and adapt 1D relaxation bounds based on linear programming (LP) into the constraint propagation process of the CP. Using the dichotomic search procedure the developed methods for OPP-2 are transformed for the case of SPP-2. Numerical results demonstrate the efficiency of the proposed strategies and of the combination of CP and LP-based pruning rules.     

融合文本概念化与网络表示的观点检索

观点检索是自然语言处理领域中的一个热点研究课题.现有的观点检索模型在检索过程中往往无法根据上下文将词汇进行知识、概念层面的抽象,在语义层面忽略词汇之间的语义联系,观点层面缺乏观点泛化能力.因此,提出一种融合文本概念化与网络表示的观点检索方法.该方法首先利用知识图谱分别将用户查询和文本概念化到正确的概念空间,并利用网络表示将知识图谱中的词汇节点表示成低维向量,然后根据词向量推出查询和文本的向量并用余弦公式计算用户查询与文本的相关度,接着引入基于统计机器学习的分类方法挖掘文本的观点.最后利用概念空间、网络表示空间以及观点分析结果构建特征,并服务于观点检索模型,相关实验表明,本文提出的检索模型可以有效提高多种检索模型的观点检索性能.其中,基于统一相关模型的观点检索方法在两个实验数据集上相比基准方法在MAP评价指标上分别提升了6.1%和9.3%,基于排序学习的观点检索方法在两个实验数据集上相比于基准方法在MAP评价指标上分别提升了2.3%和14.6%.     

Referee assignment in the Chilean football league using integer programming and patterns

This article uses integer linear programming to address the referee assignment problem in the First Division of the Chilean professional football league. The proposed approach considers balance in the number of matches each referee must officiate, the frequency of each referee being assigned to a given team, the distance each referee must travel over the course of a season, and the appropriate pairings of referee experience or skill category with the importance of the matches. Two methodologies are studied, one traditional and the other a pattern‐based formulation inspired by the home‐away patterns for scheduling season match calendars. Both methodologies are tested in real‐world and experimental instances, reporting results that improve significantly on the manual assignments. The pattern‐based formulation attains major reductions in execution times, solving real instances to optimality in just a few seconds, while the traditional one takes anywhere from several minutes to more than an hour.     

On aspectualizing component models

Server‐side component models such as Enterprise JavaBeans (EJB) add powerful abstractions to the bare ‘business objects’ layer in order to support a clean separation of server‐side application logic from other concerns such as distribution, security, transaction management and persistence. An improved separation of concerns is also the main goal of aspect‐oriented programming (AOP). This paper compares the two approaches and reasons about the possibility of substituting (parts of) component models using AOP mechanisms. We conclude that AOP is a promising approach to eliminate important shortcomings of the container‐based component approach. However, our analysis of concrete aspect‐oriented languages shows that current AOP technology is not yet mature enough to supersede component models. Copyright © 2003 John Wiley & Sons, Ltd.     

Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval

Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen classes. This paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these issues. In the proposed work, class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class clusters. And other instances are treated as negatives within the same cluster. Then a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded space. The proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results (85.38% recall@1 for CARS-196% and 70.13% recall@1 for CUB-200) compared to other existing methods.     

Architecting and implementing distributed Web applications using the graph‐oriented approach

This paper presents a graph‐oriented framework, called WebGOP, for architecture modeling and programming of Web‐based distributed applications. WebGOP is based on the graph‐oriented programming (GOP) model, under which the components of a distributed program are configured as a logical graph and implemented using a set of operations defined over the graph. WebGOP reshapes GOP with a reflective object‐oriented design, which provides powerful architectural support in the World Wide Web environment. In WebGOP, the architecture graph is reified as an explicit object which itself is distributed over the network, providing a graph‐oriented context for the execution of distributed applications. The programmer can specialize the type of graph to represent a particular architecture style tailored for an application. WebGOP also has built‐in support for flexible and dynamic architectures, including both planned and unplanned dynamic reconfiguration of distributed applications. We describe the WebGOP framework, a prototypical implementation of the framework on top of SOAP, and a performance evaluation of the prototype. The prototype demonstrated the feasibility of our approach. Results of the performance evaluation showed that the overhead introduced by WebGOP over SOAP is reasonable and acceptable. Copyright © 2003 John Wiley & Sons, Ltd.     

Optimizing disassembly processes subjected to sequence-dependent cost

Detection of the optimum disassembly sequence for a given product can proceed via mathematical programming, which is based on the AND/OR graph representation of its disassembly process. This is called the exact method for it reveals the global optimum. This paper describes an extension of the exact method in case sequence-dependent costs are considered. Previously presented methods confined themselves either to sequential disassembly, or were based on heuristics. The only exact method for the full problem known so far, needs an elaborate transformation of the AND/OR graph, and is based on integer linear programming. This paper discusses an alternate approach that uses a binary integer linear programming approach and that lacks the need of transforming the AND/OR graph. The proposed method is applied to arbitrary instances of some product structures that have been taken from the literature. Apart from this, the method is applied to an expandable AND/OR graph, that enables gradual increase of product complexity. It is demonstrated that the convergence of the iteration process is satisfactory, and the required CPU time appears comparatively small and only moderately increases with the number of constraints. It appears that the method applies to products with a complexity that cannot be managed with the integer linear programming model. The iterative method is promising for dealing with modularized products and as a benchmark for heuristic algorithms, which are used if products exhibit still higher complexity.     

基于邻近序列的IP地址地理定位方法

IP地址地理定位旨在准确地确定给定的IP地址的物理空间位置,通常采用基于测量的技术或者基于数据分析的技术。现有的基于数据分析的IP地址地理定位技术,对IP地址之间的关系考虑较少。考虑到IP地址的聚集特性,提出了一种基于邻近序列的IP地址地理定位方法。首先计算IP地址的邻近序列,并将其转化为对应的经纬度序列,然后建立模型并求解。以IP地址定位库和含有GPS信息的移动流量数据为原始数据,对该方法进行了实验验证。实验结果表明,通过邻近IP序列确实可以确定IP地址的物理空间位置,平均定位误差在20～30km,实现了区县一级的定位。该方法给IP地址地理定位问题提供了新的解决方案,同时该方法也可以与其他基于测量或者基于数据分析的方法相结合,以获得更优的结果。     

Improvements of constraint programming and hybrid methods for scheduling of tests on vehicle prototypes

In the automotive industry, a manufacturer must perform several hundreds of tests on prototypes of a vehicle before starting its mass production. Tests must be allocated to suitable prototypes and ordered to satisfy temporal constraints and various kinds of test dependencies. The manufacturer aims to minimize the number of prototypes required. We present improvements of constraint programming (CP) and hybrid approaches to effectively solve random instances from an existing benchmark. CP mostly achieves better solutions than the previous heuristic technique and genetic algorithm. We also provide customized search schemes to enhance the performance of general search algorithms. The hybrid approach applies mixed integer linear programming (MILP) to solve the planning part and CP to find the complete schedule. We consider several logical principles such that the MILP model can accurately estimate the prototype demand, while its size particularly for large instances does not exceed memory capacity. Moreover, the robustness is alleviated when we allow CP to partially change the allocation obtained from the MILP model. The hybrid method can contribute to optimal solutions in some instances.     

基于改进多嵌入空间的实时语义数据流推理

将语义数据流处理引擎与知识图谱嵌入表示学习相结合,可以有效提高实时数据流推理查询性能,但是现有的知识表示学习模型更多关注静态知识图谱嵌入,忽略了知识图谱的动态特性,导致难以应用于实时动态语义数据流推理任务。为了使知识表示学习模型适应知识图谱的在线更新并能够应用于语义数据流引擎,建立一种基于改进多嵌入空间的动态知识图谱嵌入模型PUKALE。针对传递闭包等复杂推理场景,提出3种嵌入空间生成算法。为了在进行增量更新时更合理地选择嵌入空间,设计2种嵌入空间选择算法。基于上述算法实现PUKALE模型,并将其嵌入数据流推理引擎CSPARQL-engine中,以实现实时语义数据流推理查询。实验结果表明,与传统的CSPARQL和KALE推理相比,PUKALE模型的推理查询时间分别约降低85%和93%,其在支持动态图谱嵌入的同时能够提升实时语义数据流推理准确率。     

A graph grammar‐based approach for graph layout

As a two‐dimensional formal tool, graph grammars are capable of handling the layout problems of visual programming languages. Based on an edge‐based graph grammar (EGG), this paper proposes a novel layout approach that uses the unique features of EGG and overcomes the weakness of existing layout approaches. In order to make the approach rigorous yet concise, the graph grammar mechanisms with layout constraints and quantitative analysis techniques are combined together as an integrity. First, the basic notions of EGG are briefly introduced; second, the layout approach is presented that consists of two phases, ie, bottom‐up parsing and top‐down derivation. Finally, a case study is given by taking the standard flowchart as an example to demonstrate the working process of the proposed approach.     

SAT‐based and CP‐based declarative approaches for Top‐Rank‐K closed frequent itemset mining

Top‐Rank‐K Frequent Itemset (or Pattern) Mining (FPM) is an important data mining task, where user decides on the number of top frequency ranks of patterns (itemsets) they want to mine from a transactional dataset. This problem does not require the minimum support threshold parameter that is typically used in FPM problems. Rather, the algorithms solving the Top‐Rank‐K FPM problem are fed with $K$, the number of frequency ranks of itemsets required, to compute the threshold internally. This paper presents two declarative approaches to tackle the Top‐Rank‐K Closed FPM problem. The first approach is Boolean Satisfiability‐based (SAT‐based) where we propose an effective encoding for the problem along with an efficient algorithm employing this encoding. The second approach is CP‐based, that is, utilizes Constraint Programming technique, where a simple CP model is exploited in an innovative manner to mine the Top‐Rank‐K Closed FPM itemsets from transactional datasets. Both approaches are evaluated experimentally against other declarative and imperative algorithms. The proposed SAT‐based approach significantly outperforms IM, another SAT‐based approach, and outperforms the proposed CP‐approach for sparse and moderate datasets, whereas the latter excels on dense datasets. An extensive study has been conducted to assess the proposed approaches in terms of their feasibility, performance factors, and practicality of use.     

A comparison of concurrent programming and cooperative multithreading under load balancing applications

Two models of thread execution are the general concurrent programming execution model (CP) and the cooperative multithreading execution model (CM). CP provides nondeterministic thread execution where context switches occur arbitrarily. CM provides threads that execute one at a time until they explicitly choose to yield the processor. This paper focuses on a classic application to reveal the advantages and disadvantages of load balancing during thread execution under CP and CM styles; results from a second classic application were similar. These applications are programmed in two different languages (SR and Dynamic C) on different hardware (standard PCs and embedded system controllers). An SR‐like run‐time system, DesCaRTeS, was developed to provide interprocess communication for the Dynamic C implementations. This paper compares load balancing and non‐load balancing implementations; it also compares CP and CM style implementations. The results show that in cases of very high or very low workloads, load balancing slightly hindered performance; and in cases of moderate workload, both SR and Dynamic C implementations of load balancing generally performed well. Further, for these applications, CM style programs outperform CP style programs in some cases, but the opposite occurs in some other cases. This paper also discusses qualitative tradeoffs between CM style programming and CP style programming for these applications. Copyright © 2004 John Wiley & Sons, Ltd.