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With the advent of big data era, it is more and more acceptable that the topology of a network can reveal more than we can conceive. In this paper, we propose an algorithm to predict a winner of an election among several competitors based on the relationships of individuals in a social network. Convergence is proved and an extensive experiment is done to show the effectiveness of the algorithm. Our algorithm can also be used to identify members of different parties in a fairly reasonable manner. Furthermore, our algorithm is merely based on the topology of the network, which saves us a large amount of work from collecting voting intentions and executing data pre-processing. [ABSTRACT FROM AUTHOR]

Based on wavelet transform and chaos algorithm, this paper presents a Network Traffic Forecasting Combination Model. The model introduces chaos algorithm for training the BP network and optimizing weights so as to avoid gradient descent algorithm that slowly converges and likely obtains local optimum results. Before forecasting, we first perform wavelet decomposition on the pretreated flow. Then, we utilize the FARIMA model and the improved Elman neural network model to forecast according to approximate components and detailed components, respectively. At last, we use the combination model for the network traffic forecasting. Simulation results confirmed the improved accuracy of the model, and comparing to traditional FARIMA model and wavelet neural network (WNN) model, the model can reduce the deviation. [ABSTRACT FROM AUTHOR]

Trajectory representation model has been proposed to describe non-rigid deformation. An optimal trajectory space finding algorithm for 3D non-rigid structure from motion (OTSF-NRSFM) based on this model also has been proposed. However, the influence of the wavelet basis selection on the OTSF-NRSFM algorithm has still not been studied. To help OTSF-NRSFM researchers select wavelet basis properly, we investigated the influences of wavelet basis selection. Two typical wavelet bases, DCT basis and WHT basis, are discussed in this paper. The spectrum properties of wavelet basis and feature point trajectory, trajectory representation results on synthetic shark data, OTSF-NRSFM reconstruction results on synthetic data and real data are analyzed. The results show that the wavelet selection has much influence on OTSF-NRSFM reconstruction results of some non-rigid feature points, which have complicated trajectory. This paper gives researchers some inspiration about wavelet basis selection in OTSF-NRSFM algorithm. [ABSTRACT FROM AUTHOR]

This paper develops a multi-objective multi-layer location-pricing (MLLP) model with congested facilities in which the facilities act like a classic queuing system. The customers who arrive to this system receive service at all layers in a predetermined order to fulfill their demands. The goal is to determine (1) optimal number of the facilities required at each layer, (2) optimal allocation of customers to facilities, and (3) optimal price of providing service at each layer. The objective functions are to maximize the total profit of the system and to minimize the sum of travel and waiting times, simultaneously. The problem is formulated as a multi-objective nonlinear integer mathematical programming model. Since the problem is hard to be solved analytically, we present a multi-objective meta-heuristic algorithm (MHA) based on an electromagnetism-like mechanism (ELM) as a solution for multi-objective MLLP. This algorithm used an elitist mechanism to strengthen the structure of search engine in order to find better quality solutions. The results indicate the efficiency and effectiveness of the proposed algorithm in comparison with the traditional ELM. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

Neural network was introduced to sneak circuit analysis (SCA) in previous works. However, it may generate suspect results which were hard to explain. To overcome the shortcomings, this paper proposed a novel neural network model based on circuit architecture, named CArNN, which is used as an individual of an ensemble. In CArNN, neurons represented system components, and weights represented the joints between components. Models of neurons are sigmoid functions. Clone selection algorithm was used to train CArNNs population. The trained antibodies were used as individuals of an ensemble. The inputs of CArNN are states of switches, and the outputs are states of functional components. Ensemble predicted all possible functions of circuit. The sneak circuits can be discovered by comparing the predicted and designed functions. The results revealed that CArNNs can exactly discover sneak circuits. [ABSTRACT FROM AUTHOR]

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid genetic algorithm to obtain maximum likelihood estimates of parameters in structural econometric models with multiple equilibria. The algorithm combines a pseudo maximum likelihood (PML) procedure with a genetic algorithm (GA). The GA searches globally over the large space of possible combinations of multiple equilibria in the data. The PML procedure avoids the computation of all the equilibria associated with every trial value of the structural parameters. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of time-consistency of the subcore in a multistage TU-cooperative game. We propose necessary and sufficient conditions for the time-consistency of an imputation from the subcore. Based on these conditions, we suggest an algorithm providing time-consistency of a selector of the subcore. Besides, we prove consistency of the subcore with respect to the MDM-reduction. Finally we introduce the notions of reduced game and dynamic consistency for multistage cooperative games. One of the main results of this paper is a theorem stating some properties of dynamic consistency of the subcore selectors. We focus particularly on the conditions of the dynamic consistency of the subcore with respect to the MDM-reduced game. [ABSTRACT FROM AUTHOR]

Music plays a prominent role in society and companies have even started studying its aspects for commercial purposes. It is only natural to ask what characteristics make certain songs appealing. While much research has been conducted on the mathematical principles of sound, there has been less focus on analyzing the structure of popular songs from a mathematical perspective. One mathematical tool that researchers have used to study musical structure is seriation, ordering. This paper applies several types of seriation algorithms to conduct a mathematical analysis of the structural qualities of several musical pieces. This paper focuses on 10 popular artists and their musical influences. The artists chosen for this research are linked because of the influences they cite, musical genre, and the popularity of their music. Results show that an artist's songs have a higher quantitatively measured connection with the artists they cite as influences rather than the artists who they never mention as musical influences. [ABSTRACT FROM AUTHOR]

Let G be a directed graph associated with a weight w : E (G) → R + . For an edge-cut Q of G , the average weight of Q is denoted and defined as w ave (Q) = ∑ e ∈ Q w (e) | Q | . An optimal edge-cut with average weight is an edge-cut Q such that w ave (Q) is maximum among all edge-cuts (or minimum, symmetrically). In this paper, a polynomial algorithm for this problem is proposed for finding an optimal edge-cut in a rooted tree separating the root and the set of all leafs. This algorithm enables us to develop an automatic clustering method with more accurate detection of community output. [ABSTRACT FROM AUTHOR]

In this paper, we consider the discrete unit square cover (DUSC) problem as follows: given a set 𝒫 of n points and a set 𝒮 of m axis-aligned unit squares in ℝ 2 , the objective is (i) to check whether the union of the squares in 𝒮 covers all the points in 𝒫 , and (ii) if the answer is yes, then select a minimum cardinality subset 𝒮 ∗ ⊆ 𝒮 such that each point in 𝒫 is covered by at least one square in 𝒮 ∗ . For the DUSC problem: (i) we propose a (2 + 4 k − 2) -approximation algorithm, where k (> 2) is an integer parameter that defines a trade-off between the running time and the approximation factor of the algorithm. The running time of our proposed algorithm is O (k m k n + n log n). Our solution of the DUSC problem is based on a simple (1 + 2 k − 2) -approximation algorithm for the subproblem strip square cover (SSC) problem, where all the points in 𝒫 are lying within a horizontal strip of unit height. (ii) we also propose a 2-approximation algorithm, which runs in O (m 4 n) time. The 2-approximation algorithm is based on an algorithm for the subproblem SSC problem. The algorithm for the subproblem is developed using plane sweep and graph search traversal techniques. We also extend this algorithm to get 2-approximation result for the weighted DUSC problem where the squares are assigned weights, and the aim is to choose a subset 𝒮 ∗ ⊆ 𝒮 such that each point in 𝒫 is covered by at least one square in 𝒮 ∗ and the sum of the weights of squares in 𝒮 ∗ is minimized. [ABSTRACT FROM AUTHOR]

An m-polar fuzzy model is a generalization of the fuzzy model. This model is useful for multi-polar information, multi-agent, multi-attribute and multi-object network models. In this research paper, we first introduce certain new concepts, including m-polar fuzzy digraphs, m-polar fuzzy-out neighborhood, m-polar fuzzy-in neighborhood, m-polar fuzzy competition graphs and m-polar fuzzy k-competition graphs. Moreover, we describe the construction of m-polar fuzzy competition graphs of certain products of m-polar fuzzy digraphs from their respective m-polar fuzzy competition graphs. We present some applications of m-polar fuzzy competition graphs in food webs. Finally, we present certain algorithms for computing strength of competition between species. [ABSTRACT FROM AUTHOR]

The random (2 + p)-SAT model has been proposed [18] to study the possible relation between the 'order' of phase transitions and computational complexity. It was also claimed that there exists pc > 0, such that for p < pc the random (2 + p)-SAT instance behaves like 2-SAT. Later, Achlioptas et al. [3] obtained the first rigorous results that 0.4 ≤ pc ≤ 0.695, the methods they use are the first moment method and the simple Unit-Clause algorithm. In this paper, we try to optimize the local maximality condition of the truth assignments when implementing the first moment method. We prove that the phase transition point of clauses-to-variables ratio r (dependent on p) can be improved. Moreover, we show that the upper bound of pc can be reduced to 0.6846. This fact implies that, for a constant λ < 1, a random (2 + p)-SAT formula with λn 2-clauses and 2.17 n 3-clauses is almost surely unsatisfiable. [ABSTRACT FROM AUTHOR]

This paper presents a study of two-dimensional hexagonal cellular automata (CA) with periodic boundary. Although the basic construction of a cellular automaton is a discrete model, its global level behavior at large times and on large spatial scales can be a close approximation to a continuous system. Meanwhile CA is a model of dynamical phenomena that focuses on the local behavior which depends on the neighboring cells in order to express their global behavior. The mathematical structure of the model suggests the importance of the algebraic structure of cellular automata. After modeling the dynamical behaviors, it is sometimes an important problem to be able to move backwards on CAs in order to understand the behaviors better. This is only possible if cellular automaton is a reversible one. In the present paper, we study two-dimensional finite CA defined by hexagonal local rule with periodic boundary over the field ℤ3 (i.e. 3-state). We construct the rule matrix corresponding to the hexagonal periodic cellular automata. For some given coefficients and the number of columns of hexagonal information matrix, we prove that the hexagonal periodic cellular automata are reversible. Moreover, we present general algorithms to determine the reversibility of 2D 3-state cellular automata with periodic boundary. A well known fact is that the determination of the reversibility of a two-dimensional CA is a very difficult problem, in general. In this study, the reversibility problem of two-dimensional hexagonal periodic CA is resolved completely. Since CA are sufficiently simple to allow detailed mathematical analysis, also sufficiently complex to produce chaos in dynamical systems, we believe that our construction will be applied many areas related to these CA using any other transition rules. [ABSTRACT FROM AUTHOR]

This paper proposes an uncertain multi-period bi-level network interdiction problem with uncertain arc capacities. It is proved that there exists an equivalence relationship between uncertain multi-period network interdiction problem and the obtained deterministic correspondent. Application of the generalized Benders' decomposition algorithm is considered as the solution approach to the resulting mixed-integer nonlinear programming problem. Finally, a numerical example is presented to illustrate the model and the algorithm. [ABSTRACT FROM AUTHOR]

A novel variational multiphase level set mathematical model is derived for image segmentation with two contributions. By virtue of eliminating the time-consuming re-initialization procedure and neglecting the property of the level set function during the evolution process, we in this paper present two strategies that may be taken as our contributions to solving these problems. Two scenarios are considered, namely, first, the distance regularization term which is defined by double-well potential function with two minimum points is introduced to our mathematical model for avoiding the re-initialization process. Second, by combining a Tikhonov-like regularization term which can guarantee the smoothness for the evolution curve over the previous method. Numerical simulation studies are presented to verify our new model via evaluating and comparing with existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we study to what extent decidability is connected to universality. A natural context for such a study is provided by the axiomatic theory of computability, automata and algorithms. In Section 2, we introduce necessary concepts, constructions, axioms, postulates, conditions and problems. Section 3 contains the main results of the paper. In particular, it is demonstrated (Theorems 1 and 2) that undecidability of algorithmic problems does not depend on existence of universal algorithms and may be caused by weaker conditions. At the same time, results of Theorems 2 and 3 demonstrate that decidability is incompatible with universality. It is also proved (Theorem 5) that sufficiently big classes of total algorithms/automata, such as the class of all primitive recursive functions, cannot have universal algorithms/automata. [ABSTRACT FROM AUTHOR]

This paper discusses the exact simulation of the stock price process underlying the 3/2 model. Using a result derived by Craddock and Lennox using Lie Symmetry Analysis, we adapt the Broadie-Kaya algorithm for the simulation of affine processes to the 3/2 model. We also discuss variance reduction techniques and find that conditional Monte Carlo techniques combined with quasi-Monte Carlo point sets result in significant variance reductions. [ABSTRACT FROM AUTHOR]

In this paper, a new classification method (ADCC) for high-dimensional data is proposed. In this method, a decision cluster classification (DCC) model consists of a set of disjoint decision clusters, each labeled with a dominant class that determines the class of new objects falling in the cluster. A cluster tree is first generated from a training data set by recursively calling a variable weighting k-means algorithm. Then, the DCC model is extracted from the tree. Various tests including Anderson-Darling test are used to determine the stopping condition of the tree growing. A series of experiments on both synthetic and real data sets have been conducted. Their results show that the new classification method (ADCC) performed better in accuracy and scalability than existing methods like k-NN, decision tree and SVM. ADCC is particularly suitable for large, high-dimensional data with many classes. [ABSTRACT FROM AUTHOR]

Group decision making is an important category of problem solving techniques for complicated problems, among which the Delphi method has been widely applied. In this paper an improved Delphi method based on Cloud model is proposed in order to deal with the fuzziness and uncertainty in experts' subjective judgments. The proposed Cloud Delphi Method (CDM) describes experts' opinions by Cloud model and we aggregate the experts' Cloud opinions by synthetic algorithm and weighted average algorithm. Another key point of CDM is to stabilize and accommodate the individual fuzzy estimates by the defined stability rules rather than having to force them to converge, or reduce. The Cloud opinions and aggregation results can be exhibited in a graphically way leading experts to judge intuitively and it can decrease the number of repetitive surveys and/or interviews. Moreover, it is more scientific and easier to represent experts' opinion base on Cloud model which can combine fuzziness and uncertainty well. A numerical example is examined to demonstrate applicability and implementation process of CDM. [ABSTRACT FROM AUTHOR]

LDA (Latent Dirichlet Allocation) proposed by Blei is a generative probabilistic model of a corpus, where documents are represented as random mixtures over latent topics, and each topic is characterized by a distribution over words, but not the attributes of word positions of every document in the corpus. In this paper, a Word Position-Related LDA Model is proposed taking into account the attributes of word positions of every document in the corpus, where each word is characterized by a distribution over word positions. At the same time, the precision of the topic-word's interpretability is improved by integrating the distribution of the word-position and the appropriate word degree, taking into account the different word degree in the different word positions. Finally, a new method, a size-aware word intrusion method is proposed to improve the ability of the topic-word's interpretability. Experimental results on the NIPS corpus show that the Word Position-Related LDA Model can improve the precision of the topic-word's interpretability. And the average improvement of the precision in the topic-word's interpretability is about 9.67%. Also, the size-aware word intrusion method can interpret the topic-word's semantic information more comprehensively and more effectively through comparing the different experimental data. [ABSTRACT FROM AUTHOR]

Resistance spot welding is a major metal connecting method in vehicle and other domestic electronic domains. Among all the welding techniques, the spot welding inverter is an important direction at the present time. The high nonlinearity and strongly coupled multiple parameters in the resistance spot welding process challenge the classical control theory based on some specific conditions and ideal assumptions, which in real practice obstacle the high-quality welding. This paper put the fuzzy neural network into a constant-current-controlled spot welding inverter, where the welding current peak and its variation are adopted as the input parameters and the duty ratio of the switches is regarded as the output. Eventually a five-layer feed-forward network was constructed, back propagation (BP) algorithm was applied to revise the adjustable parameters in the network, and a mathematical model was established to obtain the training samples serving for the network. The ultimate precision could reach 1.75%, the relative control error is 2.28% with strong external disturbances, the overmodulation is 3.35%, and the total modulating period is seven switching period, which indicated that the proposed algorithm has good performance. [ABSTRACT FROM AUTHOR]

This paper proposes a new method of controlling quantum systems via probability density function (PDF) control. Based on the quantum model from the PDF perspective, two specific control algorithms are proposed for the general case and limited input energy, respectively. Unlike traditional quantum control methods, this method directly controls the probability distribution of the quantum state. It provides an alternative method for quantum control engineering. [ABSTRACT FROM AUTHOR]

The contouring precision of computer numerical control (CNC) machine tools relates to whether the dynamic performances among all the axes match or not. In general, the cross-coupled controller (CCC) is a more efficient approach to enhance contouring precision than single-axis servo controller. Note that the contouring error CCC can not be separated from pathway programming in multi-axis linked servo system. In order to obtain satisfied contouring precision, the CCC approach connected with non-uniform rational basis spline (NURBS) curve interpolation is researched in detail in the paper. Above all, an "arc approximation algorithm" contouring error computing model is developed, according to the real cutter positions from each axis feedback and the interpolation dots stored in the interpolation buffer. Then the contouring error correction quantity PI control and distribution algorithm is put forward by introducing a zoom coefficient. The contrast experimental results on the CNC experiment table show that the developed CCC approach can reduce contouring error and improve tracking performance further. [ABSTRACT FROM AUTHOR]

We consider the problem of enriching the travelogue associated with a small number (even one) of images with more web images. Images associated with the travelogue always consist of the content and the style of textual information. Relying on this assumption, in this paper, we present a framework of travelogue enriching, exploiting both textual and visual information generated by different users. The framework aims to select the most relevant images from automatically collected candidate image set to enrich the given travelogue, and form a comprehensive overview of the scenic spot. To do these, we propose to build two-layer probabilistic models, i.e. a text-layer model and image-layer models, on offline collected travelogues and images. Each topic (e.g. Sea, Mountain, Historical Sites) in the text-layer model is followed by an image-layer model with sub-topics learnt (e.g. the topic of sea is with the sub-topic like beach, tree, sunrise and sunset). Based on the model, we develop strategies to enrich travelogues in the following steps: (1) remove noisy names of scenic spots from travelogues; (2) generate queries to automatically gather candidate image set; (3) select images to enrich the travelogue; and (4) choose images to portray the visual content of a scenic spot. Experimental results on Chinese travelogues demonstrate the potential of the proposed approach on tasks of travelogue enrichment and the corresponding scenic spot illustration. [ABSTRACT FROM AUTHOR]

The goal of this paper is to solve mathematical model equations on solid tumour growth and compute their parameter values by applying growth rates of prostate cancer cell lines in vivo. For these computations, we investigate previously developed C3(1)/Tag transgenic models of prostate cancer. To make the computations fast, we have constructed an algorithm, which is based on small amounts of spatial grid-points and obtained a correspondence between the in vivo growth of tumours and the solutions of the model equations. [ABSTRACT FROM AUTHOR]

Fuzzy control has been successfully applied in many cases to which conventional control algorithms are difficult to be applied. Recently, it was proven that fuzzy systems are capable of approximating any real continuous function to arbitrary accuracy. This result motivates us to use the fuzzy identifiers for nonlinear dynamic systems and then design the fuzzy supervised nonlinear PID controller based on the fuzzy system. There are two main objectives in this paper: (1) We use the Takagi and Sugeno's fuzzy models as an identifier for nonlinear dynamic systems, and derive the identification algorithm, (2) the fuzzy supervisor design method for tracking control is proposed based on this fuzzy system. In order to improve the dynamic response of the closed loop fuzzy model, the optimization of the performance of the fuzzy supervisor will be considered. To prove the potential applications of the proposed strategy, simulation was carried out for the speed control of a DC motor with serial excitation and a first order nonlinear process. [ABSTRACT FROM AUTHOR]

In this paper, we address the scheduling model with discretely compressible release dates, where processing any job with a compressed release date incurs a corresponding compression cost. We consider the following problem for the first time: scheduling with discretely compressible release dates to minimize the sum of makespan plus total compression cost. We show its NP-hardness, and design an approximation algorithm with worst-case performance ratio 2, which is the best possible if the processing order of the jobs is pre-specified. [ABSTRACT FROM AUTHOR]

In this paper, we examine the wavelet characterization of Hardy space H1, and show that the H1-norm is a good choice for modelizing the oscillating patterns. Furthermore, we give the discrete representation of H1-norm by using wavelet coefficients, and apply it to the variational image decomposition models. Finally, we give the iterative algorithm, and present various numerical results on images to demonstrate the potential of our methods. [ABSTRACT FROM AUTHOR]

The generation of Pareto optimal solutions for complex systems with multiple conflicting objectives can be easier if the problem can be decomposed and solved as a set of smaller coordinated subproblems. In this paper, a new decomposition-coordination method is proposed, where the global problem is partitioned into subsystems on the basis of the connection structure of the mathematical model, assigning a relative importance to each of them. In order to obtain Pareto optimal solutions for the global system, the aforementioned subproblems are coordinated taking into account their relative importance. The scheme that has been developed is an iterative one, and the global efficient solutions are found through a continuous information exchange process between the coordination level (upper level) and the subsystem level (lower level). Computational experiments on several randomly generated problem instances show that the suggested algorithm produces efficient solutions within reasonable computational times. [ABSTRACT FROM AUTHOR]

Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining. [ABSTRACT FROM AUTHOR]

This paper addresses a novel sentence reduction algorithm based on a decision tree model in which semantic information is used to enhance the accuracy of sentence reduction. The proposed algorithm is able to deal with the changeable order problem in sentence reduction. Furthermore, the use of decision list to solve the fragment problem in sentence reduction based decision tree model is also discussed. Our experimental results show an improvement when compared with earlier methods. [ABSTRACT FROM AUTHOR]

This paper proposes a new reliability model, circular consecutive 2-out-of-(r,r)-from-(n,n):F model. In this model, the system consists of a square grid of side n (containing of n2 components) such that the system fails if and only if there is at least one square of side r which includes among them at least two failed components. For i.i.d. case an algorithm is given for computing the reliability of circular system. The reliability function can be expressed by the number of 0–1 matrices having no two or more 0s at any square of side r. [ABSTRACT FROM AUTHOR]

The problem of outlier and change-point identification has received considerable attention in traditional linear regression models from both, classical and Bayesian standpoints. In contrast, for the case of regression models with measurement errors, also known as error-in-variables models, the corresponding literature is scarce and largely focused on classical solutions for the normal case. The main object of this paper is to propose clustering algorithms for outlier detection and change-point identification in scale mixture of error-in-variables models. We propose an approach based on product partition models (PPMs) which allows one to study clustering for the models under consideration. This includes the change-point problem and outlier detection as special cases. The outlier identification problem is approached by adapting the algorithms developed by Quintana and Iglesias [32] for simple linear regression models. A special algorithm is developed for the change-point problem which can be applied in a more general setup. The methods are illustrated with two applications: (i) outlier identification in a problem involving the relationship between two methods for measuring serum kanamycin in blood samples from babies, and (ii) change-point identification in the relationship between the monthly dollar volume of sales on the Boston Stock Exchange and the combined monthly dollar volumes for the New York and American Stock Exchanges. [ABSTRACT FROM AUTHOR]

A recent paper by Matuttis and Ito questions the numerical accuracy of a widely-used fermion Monte Carlo algorithm. They also claim that the increase in the d-wave pairfield susceptibility χd(T) of a doped 4×4 Hubbard model at low temperature, previously found using this algorithm, is an artifact due to numerical errors. Here, we provide tests which show that this algorithm is numerically accurate and show that the simulation of χd for a 2×2 lattice agrees with exact diagonalization results. We also provide more complete data for χd on a 4×4 lattice that is consistent with our previous results. [ABSTRACT FROM AUTHOR]

This paper develops a mathematical model to derive the optimal preventive maintenance warranty (PMW) policy for repairable products with age-dependent maintenance costs. Under a PMW, any product failures are rectified by minimal repair, and additional preventive maintenance actions are carried out within the warranty period. When the costs for preventive maintenance and minimal repair are age-dependent, the optimal number of preventive maintenance actions, corresponding maintenance degrees, and the maintenance schedule for designing a PMW policy are derived here such that the expected total warranty cost is minimized. Under some reasonable conditions, we show that there exists a unique optimal PMW policy in which the product is maintained periodically with the same preventive maintenance degree. Using this property, an efficient algorithm is provided to search for the optimal policy. Some related models developed in the literature are discussed and these models are in fact special cases of the model proposed in this paper. Furthermore, when the life-time distribution of a product is Weibull, a closed-form expression of the optimal policy is obtained. Finally, the impact of providing preventive maintenance is evaluated through numerical examples. [ABSTRACT FROM AUTHOR]

Software reuse is an important topic due to its potential benefits in increasing product quality and decreasing cost. Although more and more people are aware that not only technical issues, but also nontechnical issues are important to the success of software reuse, people are still not certain which factors will have direct effect on the success of reuse. In this paper, we applied a causal discovery algorithm to the software reuse survey data [2]. Ensemble strategy is incorporated to locate a probable causal model structure for software reuse, and find all those factors which have direct effect on the success of reuse. Our discovery results reinforced some conclusions of Morisio et al. and found some new conclusions which might significantly improve the odds of a reuse project succeeding. [ABSTRACT FROM AUTHOR]

A variation on the classical polygon illumination problem was introduced in [Aichholzer et al. EuroCG'09]. In this variant light sources are replaced by wireless devices called -modems, which can penetrate a fixed number , of 'walls'. A point in the interior of a polygon is 'illuminated' by a -modem if the line segment joining them intersects at most edges of the polygon. It is easy to construct polygons of vertices where the number of -modems required to illuminate all interior points is . However, no non-trivial upper bound is known. In this paper we prove that the number of kmodems required to illuminate any polygon of vertices is . For the cases of illuminating an orthogonal polygon or a set of disjoint orthogonal segments, we give a tighter bound of . Moreover, we present an time algorithm to achieve this bound. [ABSTRACT FROM AUTHOR]

In this paper, inspired by the pseudo-fractal networks (PFN) and the delayed pseudo-fractal networks (DPFN), we present a novel delayed pseudo-fractal networks model, denoted by NDPFN. Different from the generation algorithm of those two networks, every edge of the novel model has a time-delay to generate new nodes after producing one node. We derive exactly the main structural properties of the novel networks: degree distribution, clustering coefficient, diameter and average path length. Analytical results show that the novel networks have small-world effect and scale-free topology. Comparing topological parameters of these three networks, we find that the degree exponent of the novel networks is the largest while the clustering coefficient and the average path length are the smallest. It means that this kind of delay could weaken the heterogeneity and the small-world features of the network. Particularly, the delay effect in the NDPFN is contrary to that in the DPFN, which illustrates the variety of delay method could produce different effects on the network structure. These present findings may be helpful for a deeper understanding of the time-delay influence on the network topology. [ABSTRACT FROM AUTHOR]

A finite word w ∈ {0, 1}* is balanced if for every equal-length factors u and v of every cyclic shift of w we have ||u|1 - |v|1| ≤ 1. This new class of finite words was defined in [O. Jenkinson and L. Q. Zamboni, Characterisations of balanced words via orderings, Theoret. Comput. Sci. 310(1-3) (2004) 247-271]. In [O. Jenkinson, Balanced words and majorization, Discrete Math. Algorithms Appl. 1(4) (2009) 463-484], there was proved several results considering finite balanced words and majorization. One of the main results was that the base-2 orbit of the balanced word is the least element in the set of orbits with respect to partial sum. It was also proved that the product of the elements in the base-2 orbit of a word is maximized precisely when the word is balanced. It turns out that the words 0q-p1p have similar extremal properties, opposite to the balanced words, which makes it meaningful to call these words the most unbalanced words. This paper contains the counterparts of the results mentioned above. We will prove that the orbit of the word u = 0q-p1p is the greatest element in the set of orbits with respect to partial sum and that it has the smallest product. We will also prove that u is the greatest element in the set of orbits with respect to partial product. [ABSTRACT FROM AUTHOR]

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems. [ABSTRACT FROM AUTHOR]

This paper introduces a new disambiguation algorithm for finite automata and functional finite-state transducers. It gives a full description of this algorithm, including a detailed pseudocode and analysis, and several illustrating examples. The algorithm is often more efficient and the result dramatically smaller than the one obtained using determinization for finite automata or the construction of Schützenberger. The unambiguous automaton or transducer created by our algorithm are never larger than those generated by the construction of Schützenberger. In fact, in a variety of cases, the size of the unambiguous transducer returned by our algorithm is only linear in that of the input transducer while the transducer created by the construction of Schützenberger is exponentially larger. Our algorithm can be used effectively in many applications to make automata and transducers more efficient to use. [ABSTRACT FROM AUTHOR]

The objective of this paper is statistical comparison of modelling methods for software maintainability prediction. The statistical comparison is performed by building software maintainability prediction models using 27 different regression and machine learning based algorithms. For this purpose, software metrics datasets of two different commercial object-oriented systems are used. These systems were developed using an object oriented programming language Ada. These systems are User Interface Management System (UIMS) and Quality Evaluation System (QUES). It is shown that different measures like MMRE, RMSE, Pred(0.25) and Pred(0.30) calculated on predicted values obtained from leave one out (LOO) cross validation produce very divergent results regarding accuracy of modelling methods. Therefore the 27 modelling methods are evaluated on the basis of statistical significance tests. The Friedman test is used to rank various modelling methods in terms of absolute residual error. Six out of the ten top ranked modelling methods are common to both UIMS and QUES. This indicates that modelling methods for software maintainability predicton are solid and scalable. After obtaining ranks, pair wise Wilcoxon Signed rank test is performed. Wilcoxon Sign rank test indicates that the top ranking method in UIMS data set is significantly superior to only four other modelling methods whereas the top tanking method in QUES data set is significantly superior to 11 other modelling methods. The performance of instance based learning algorithms - IBk and Kstar is comparable to modelling methods used in earlier studies. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the solitary wave solutions for the two-dimensional modified Korteweg-de Vries-Burgers (mKdV-B) equation in shallow water model. Despite that Painlevé test fails to prove the integrability of the mKdV-B equation by using the WTC-Kruskal algorithm, the Bäcklund transformation is obtained via the truncation expansion. The exact solutions of the mKdV-B equation are found using factorization techniques, Exp-function and energy integral approach of the corresponding ordinary differential equation. We found that the dispersion relation of the linearized mKdV-B equation lies on the complex plane yielding a damping character. By keeping the water height relatively small, we illustrate the resulting solutions in several figures showing the shock and solitary wave nature in the flow. The stability for the mKdV-B equation is analyzed by using the phase plane method. [ABSTRACT FROM AUTHOR]

This paper proposes to use a state feedback method to control the Hopf bifurcation for a novel congestion control model, i.e. the exponential random early detection (RED) algorithm with a single link and a single source. The gain parameter of the congestion control model is chosen as the bifurcation parameter. The analysis shows that in the absence of the state feedback controller, the model loses stability via the Hopf bifurcation early, and can maintain a stationary sending rate only in a certain domain of the gain parameter. When applying the state feedback controller to the model, the onset of the undesirable Hopf bifurcation is postponed. Thus, the stability domain is extended, and the model possesses a stable sending rate in a larger parameter range. Furthermore, explicit formulae to determine the properties of the Hopf bifurcation are obtained. Numerical simulations are given to justify the validity of the state feedback controller in bifurcation control. [ABSTRACT FROM AUTHOR]

Iris recognition is one of the most reliable personal identification methods. This paper presents a novel algorithm for iris recognition encompassing iris segmentation, fusion of statistical and co-occurrence features extracted from the curvelet and ridgelet transformed images. In this work, the pupil and iris boundaries are detected by using the equation of circle from three points on its circumference. Using Canny edge detection, the iris radius value is empirically chosen based on rigorous experimentation. Eyelash removal is done by using a horizontal 1-D rank filter. Iris normalization is done by mapping the detected iris region from the polar domain to the rectangular domain and the multi-resolution transforms such as curvelet and ridgelet transforms are applied for multi-resolutional feature extraction. The classification is done using Manhattan distance (Md) and multiclass classifier with logistic function and the two results are compared. Here, the benchmark database CASIA-IRIS-V3 (Interval) is used for identification and recognition. It is observed that the ridgelet transform increases the iris recognition rate. [ABSTRACT FROM AUTHOR]