A space and time efficient algorithm for SimRank computation

SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing approaches for conducting SimRank computation adopt an iteration paradigm. The most efficient deterministic technique yields O(n³)O\left(n^3\right) worst-case time per iteration with the space requirement O(n²)O\left(n^2\right), where n is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration takes O (min{ n ·m , n^r })O \left(\min \left\{ n \cdot m , n^r \right\}\right) time and O ( n + m )O \left( n + m \right) space, where m is the number of edges in a web-graph model and r ≤ log₂ 7. In addition, we extend the similarity transition matrix to prevent random surfers getting stuck, and devise a pruning technique to eliminate impractical similarities for each iteration. Moreover, we also develop a reordering technique combined with an over-relaxation method, not only speeding up the convergence rate of the existing techniques, but achieving I/O efficiency as well. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness of our iteration techniques.     

Computer vision techniques for traffic flow computation

This paper describes an application of computer vision techniques to road surveillance. It reports on a project undertaken in collaboration with the Research and Innovation group at the Ordnance Survey. The project aims to produce a system that detects and tracks vehicles in real traffic scenes to generate meaningful parameters for use in traffic management. The system has now been implemented using two different approaches: a feature-based approach that detects and groups corner features in a scene into potential vehicle objects, and an appearance-based approach that trains a cascade of classifiers to learn the appearances of vehicles as an arrangement of a set of pre-defined simple Haar features. Potential vehicles detected are then tracked through an image sequence, using the Kalman filter motion tracker. Experimental results of the algorithms are presented in this paper.     

Compiler techniques for the distribution of data and computation

This paper presents a new method that can be applied by a parallelizing compiler to find, without user intervention, the iteration and data decompositions that minimize communication and load imbalance overheads in parallel programs targeted at NUMA architectures. One of the key ingredients in our approach is the representation of locality as a locality-communication graph (ICG) and the formulation of the compiler technique as a mixed integer nonlinear programming (MINLP) optimization problem on this graph. The objective function and constraints of the optimization problem model communication costs and load imbalance. The solution to this optimization problem is a decomposition that minimizes the parallel execution overhead. This paper summarizes the process of how the compiler extracts the locality information from a nonannotated code and focuses on how this compiler can derive the optimization problem, solve it, and generate the parallel code with the automatically selected iteration and data distributions. In addition, we include a discussion about our model and the solutions - the decompositions - that it provides. The approach presented in the paper is evaluated using several benchmarks. The experimental results demonstrate that the MINLP formulation does not increase compilation time significantly and that our framework generates very efficient iteration/data distributions for a variety of NUMA machines.     

Accuracy bounds and optimal computation of robot localization

We present an optimal method for estimating the current location of a mobile robot by matching an image of the scene taken by the robot with a model of the known environment. We first derive a theoretical accuracy bound and then give a computational scheme that can attain that bound, which can be viewed as describing the probability distribution of the current location. Using real images, we demonstrate that our method is superior to the naive least-squares method. We also confirm the theoretical predictions of our theory by applying the bootstrap procedure. Received: 17 March 1998 / Accepted: 7 April 2001     

Intelligent computation techniques for process planning of cold forging

In this paper, two artificial intelligence (AI) techniques were applied to the problem of process planning in multiple-blow cold forging. Given the reduction in area of the product to be forged and the degree of formability of the material, in the first application a fuzzy logic (FL) technique was used to discriminate whether or not a cold forged product was feasible in a single blow. In the second application, a neural network (NN) architecture was used to identify the correct number of blows necessary to complete the cold forging process.     

Resource holding times: computation and optimization

In scheduling hard-real-time systems, the primary objective is to meet all deadlines. We study the scheduling of such systems with the secondary objective of minimizing the duration of time for which the system locks each shared resource. We abstract out this objective into the resource hold time (rht)—the largest length of time that may elapse between the instant that a system locks a resource and the instant that it subsequently releases the resource, and study properties of the rht. We present an algorithm for computing resource hold times for every resource in a task system that is scheduled using Earliest Deadline First scheduling, with resource access arbitrated using the Stack Resource Policy. We also present and prove the correctness of algorithms for decreasing these rht’s without changing the semantics of the application or compromising application feasibility.

A survey on evolutionary computation for complex continuous optimization

Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

     

Nonsmooth optimization techniques for semisupervised classification

We apply nonsmooth optimization techniques to classification problems, with particular reference to the TSVM (Transductive Support Vector Machine) approach, where the considered decision function is nonconvex and nondifferentiable and then difficult to minimize. We present some numerical results obtained by running the proposed method on some standard test problems drawn from the binary classification literature.     

Semantic optimization techniques for preference queries

Preference queries are relational algebra or SQL queries that contain occurrences of the winnow operator (find the most preferred tuples in a given relation). Such queries are parameterized by specific preference relations. Semantic optimization techniques make use of integrity constraints holding in the database. In the context of semantic optimization of preference queries, we identify two fundamental properties: containment of preference relations relative to integrity constraints and satisfaction of order axioms relative to integrity constraints. We show numerous applications of those notions to preference query evaluation and optimization. As integrity constraints, we consider constraint-generating dependencies, a class generalizing functional dependencies. We demonstrate that the problems of containment and satisfaction of order axioms can be captured as specific instances of constraint-generating dependency entailment. This makes it possible to formulate necessary and sufficient conditions for the applicability of our techniques as constraint validity problems. We characterize the computational complexity of such problems.     

Multi-search optimization techniques

Two multi-search optimization techniques are developed. The motive is to show that increasing the search directions results in a better rate of convergence. This idea was first investigated by Miele and Cantrell [1]. Their technique is computationally attractive, but exact line-search is needed in two directions. Therefore the problem of line-search has been more complicated since at the end of each iteration two stopping functions have to satisfied. Also, built-in safeguards were used to ensure the stability of the descent process. For nonquadratic functions Miele's method (as well as many other optimization techniques) needs restarting every $\text{n}$ + 1 iterations in order to improve the convergence rate. Extending this two-direction search of Miele to an $\text{n-}\text{direction}$ search in order to further improve the rate of convergence would highly complicate the search process, since exactly $\text{n}$ line-searches are needed together with $\text{n}$ simultaneous stopping functions to be satisfied besides the resulting increase in the built-in safeguards to impractical limits. In the techniques presented the optimum step-size in each direction is obtained by simply minimizing a quadratic function of a linearized gradient, and only sufficient decrease is needed along the search directions without the need of exact line-searches. Instead, simple successive halving is used till the function starts to increase; then a new iteration is started without the use of any simultaneous stopping functions at the end of each iteration. Also, the $\text{n-}\text{direction}$ case has been developed in exactly the same fashion as the two-direction case without quiring the use of $\text{n}$ simultaneous stopping functions a property which is not possible if the method of Miele is to be extended to the $\text{n-}\text{direction}$ case. The highly effective rate of convergence obtained by using the $\text{n-}\text{direction}$ search really justifies the would-be increase in the required computational effort. Furthermore, no restarting has been needed to improve the convergence rate. The results of two tried examples show that the multi-search optimization techniques that have been developed have excellent convergence rates without endangering the stability requirement.     

Neural and fuzzy computation techniques for playout delay adaptation in VoIP networks

Playout delay adaptation algorithms are often used in real time voice communication over packet-switched networks to counteract the effects of network jitter at the receiver. Whilst the conventional algorithms developed for silence-suppressed speech transmission focused on preserving the relative temporal structure of speech frames/packets within a talkspurt (intertalkspurt adaptation), more recently developed algorithms strive to achieve better quality by allowing for playout delay adaptation within a talkspurt (intratalkspurt adaptation). The adaptation algorithms, both intertalkspurt and intratalkspurt based, rely on short term estimations of the characteristics of network delay that would be experienced by up-coming voice packets. The use of novel neural networks and fuzzy systems as estimators of network delay characteristics are presented in this paper. Their performance is analyzed in comparison with a number of traditional techniques for both inter and intratalkspurt adaptation paradigms. The design of a novel fuzzy trend analyzer system (FTAS) for network delay trend analysis and its usage in intratalkspurt playout delay adaptation are presented in greater detail. The performance of the proposed mechanism is analyzed based on measured Internet delays.     

Solving equations by hybrid evolutionary computation techniques

Evolutionary computation techniques have mostly been used to solve various optimization and learning problems. This paper describes a novel application of evolutionary computation techniques to equation solving. Several combinations of evolutionary computation techniques and classical numerical methods are proposed to solve linear and partial differential equations. The hybrid algorithms have been compared with the well-known classical numerical methods. The experimental results show that the proposed hybrid algorithms outperform the classical numerical methods significantly in terms of effectiveness and efficiency     

基于权重标准化SimRank方法的查询扩展技术研究

查询扩展是信息检索中的一项重要技术。传统的局部分析查询扩展方法利用伪相关文档作为候选词集合,然而部分伪相关文档并不具有很高的相关性。该文利用真实的搜索引擎查询日志,建立了查询点击图,经过多次图结构的转化得到能够反映词之间关联程度的词项关系图,并在图结构的相似度算法SimRank的基础上,提出了一种基于权重标准化的改进SimRank方法,该方法利用词项关系图中词项的全局和间接关系,能够有效挖掘与原始查询相关联的扩展词。同时,为降低SimRank算法的计算复杂度,该文采用了剪枝等策略进行优化,使得计算效率有大幅提高。在TREC标准数据集上的实验表明,该文的方法可以有效地选择相关扩展词。MAP指标较局部分析查询扩展方法提高了1.81%,在P@10和P@20指标评价中效果分别提高了5.44%和3.73%。     

Analog computer gradient techniques for parameter optimization

After discussing the already-known means for solving optimization problems by the maximum gradient principle, this paper then contributes several modifications to this method which permit solution by analog iteration of a large number of problems comprising several variables; including those which are limited by parameter constraints, eventually accompanied by sensitivity constraints. The factors which make up the performance index are generated simultaneously by an analog method. This paper examines as well the effects of noise and perturbations on the convergence rate of the iterative process of optimization.     

Neural techniques for combinatorial optimization with applications

After more than a decade of research, there now exist several neural-network techniques for solving NP-hard combinatorial optimization problems. Hopfield networks and self-organizing maps are the two main categories into which most of the approaches can be divided. Criticism of these approaches includes the tendency of the Hopfield network to produce infeasible solutions, and the lack of generalizability of the self-organizing approaches (being only applicable to Euclidean problems). The paper proposes two new techniques which have overcome these pitfalls: a Hopfield network which enables feasibility of the solutions to be ensured and improved solution quality through escape from local minima, and a self-organizing neural network which generalizes to solve a broad class of combinatorial optimization problems. Two sample practical optimization problems from Australian industry are then used to test the performances of the neural techniques against more traditional heuristic solutions.     

Constrained space deformation techniques for design optimization

We present a novel shape deformation method for its use in design optimization tasks. Our space deformation technique based on moving least squares approximation improves upon existing approaches in crucial aspects: It offers the same level of modeling flexibility as surface-based deformations, but it is independent of the underlying geometry representation and therefore highly robust against defects in the input data. It overcomes the scalability limitations of existing space deformation techniques based on globally supported radial basis functions while providing the same high level of deformation quality. Finally, unlike existing space deformation approaches, our technique directly incorporates geometric constraints–such as preservation of critical feature lines, circular couplings, planar or cylindrical construction parts–into the deformation, thereby fostering the exploration of more favorable and producible shape variations during the design optimization process.     

Norm optimization techniques for improved structural reanalysis

An approximate structural reanalysis method that is based on a binomial series expansion and that employs operations of matrix scaling and norm minimization is presented in this paper. It is shown that the scaling operation significantly reduces several unfavourable effects of the magnitude and nature of design changes on the prediction quality of the model. Norm minimization provides a very effective and optimal scaling factor in a simplified manner. The most significant achievement of the work is the development of an effective and efficient reanalysis model that provides near-exact response quantities even when large and strongly dispersed changes are introduced in the design. The potential capacity of the proposed method of improvement is demonstrated using numerical examples. Graphical interpretation of the concept is also provided.