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Summarizes the construction of paper folding. Method for approximating rational subdivisions or arbitrary angles and line segments; Angle-folding algorithm; Approximating angles, regular polygons and star polygons; Dissection of angles into equal parts.

In this paper, we present two nonmonotone versions of adaptive cubic regularized (ARC) method for unconstrained optimization problems. The proposed methods are a combination of the ARC algorithm with the nonmonotone line search methods introduced by Zhang and Hager [A nonmonotone line search technique and its application to unconstrained optimization, SIAM J. Optim. 14 (2004), pp. 1043–1056] and Ahookhosh et al. [A nonmonotone trust-region line search method for large-scale unconstrained optimization, Appl. Math. Model. 36 (2012), pp. 478–487]. The global convergence analysis for these iterative algorithms is established under suitable conditions. Several numerical examples are given to illustrate the efficiency and robustness of the newly suggested methods. The obtained results show the satisfactory performance of the proposed algorithms when compared to the basic ARC algorithm. [ABSTRACT FROM AUTHOR]

A subset S of V (G) is an independent dominating set of G if S is independent and each vertex of G is either in S or adjacent to some vertex of S. Let i(G) denote the minimum cardinality of an independent dominating set of G. A graph G is k-i-critical if i(G) = k, but i(G + uv) < k for any pair of non-adjacent vertices u and v of G. The problem that arises is that of characterizing k-i critical graphs. In this paper, we characterize connected 3-i-critical graphs with minimum vertex cutset of size 2. More specifically, we show that if G is a connected 3-i-critical graph with minimum vertex cutset S of size 2 and the number of components of G−S is exactly two, then G is isomorphic to a graph in one of nine classes of connected 3-i-critical graphs. The results in this paper together with results in [1] and [2] provide a complete characterization of connected 3-i-critical graphs with a minimum cutset of size at most 3. [ABSTRACT FROM AUTHOR]

The proximal method of multipliers was proposed by Rockafellar [Augmented Lagrangians and applications of the proximal point algorithm in convex programming. Math Oper Res. 1976;1:97–116] for solving convex programming and it is a kind of proximal point method applied to convex programming. In this paper, we apply this method for solving nonlinear semidefinite programming problems, in which subproblems have better properties than those from the augmented Lagrange method. We prove that, under the linear independence constraint qualification and the strong second-order sufficiency optimality condition, the rate of convergence of the proximal method of multipliers, for a nonlinear semidefinite programming problem, is linear and the ratio constant is proportional to 1/c, where c is the penalty parameter that exceeds a threshold c ∗ > 0. Moreover, the rate of convergence of the proximal method of multipliers is superlinear when the parameter c increases to + ∞. [ABSTRACT FROM AUTHOR]

In this paper we design a family of cell-average nonlinear prediction operators that make use of the generalized harmonic means and we apply the resulting schemes to image processing. The new family of nonlinear schemes conserve the numerical properties of the linear schemes, such as the L 1 -stability, the order of accuracy or compression rate but avoiding Gibbs phenomenon close to the discontinuities. The generalized harmonic mean was introduced in the framework of point-values in [A. Guessab, M. Moncayo, and G. Schmeisser, A class of nonlinear four-point subdivision schemes. Properties in terms of conditions, Adv. Comput. Math. 37 (2012), pp. 151–190] in order to improve the results of the harmonic mean. However, in the cell-average setting our conclusion is that, from a numerical point of view, the advantage of using the new mean is not clear. [ABSTRACT FROM AUTHOR]

In this paper, the Rayleigh-Lindley (RL) distribution is introduced, obtained by compounding the Rayleigh and Lindley discrete distributions, where the compounding procedure follows an approach similar to the one previously studied by Adamidis and Loukas in some other contexts. The resulting distribution is a two-parameter model, which is competitive with other parsimonious models such as the gamma and Weibull distributions. We study some properties of this new model such as the moments and the mean residual life. The estimation was approached via EM algorithm. The behavior of these estimators was studied in finite samples through a simulation study. Finally, we report two real data illustrations in order to show the performance of the proposed model versus other common two-parameter models in the literature. The main conclusion is that the model proposed can be a valid alternative to other competing models well established in the literature. [ABSTRACT FROM AUTHOR]

Conjugate gradient methods are widely used for solving unconstrained optimization and nonlinear equations, specially in large-scale cases. Since they own the attractive practical factors of simple computation and low memory requirement, interesting theoretical features of curvature information and strong global convergence. In this paper, we present a modified conjugate gradient algorithm by line search method with acceleration scheme for nonlinear symmetric equations. Furthermore, the proposed method not only possess descent property but also owns global convergence in mild conditions. Numerical results also indicate that the presented method is much more effective than the other methods for the test problems. [ABSTRACT FROM PUBLISHER]

In this paper, a multiple-time-step decoupled algorithm for a non-stationary Stokes–Darcy problem is proposed and investigated. Under a modest time step restriction of physical parameters and the time step proportion, we give the stability analysis and convergence analysis of the decoupled scheme with different time step in fluid and porous subregions. Finally, a series of numerical experiments are provided to illustrate the accuracy, efficiency, and stability of the presented method for the coupled problem with the Beavers–Joseph–Saffman–Jones interface conditions. [ABSTRACT FROM PUBLISHER]

This paper inductively investigates an inverse problem for difference boundary value problems with boundary conditions that depend quadratically on the eigenparameter. In particular, given the eigenvalues and the weights, we provide an algorithm to uniquely reconstruct the potential. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of sBCI/sBCK/eBCI/eBCK-algebras as a generalization of the notion of BCI/BCK-algebras. This structure is studied in detail. Also we introduce a way to make an eBCK-algebra from a BCK-algebra and vice versa. [ABSTRACT FROM AUTHOR]

In this paper, an optimal error estimate for system of parabolic quasi-variational inequalities related to stochastic control problems is studied. Existence and uniqueness of the solution is provided by the introduction of a constructive algorithm. An optimally L∞-asymptotic behavior in maximum norm is proved using the semiimplicit time scheme combined with the finite element spatial approximation. The approach is based on the concept of subsolution and discrete regularity. [ABSTRACT FROM AUTHOR]

In this paper, we will describe a new factorization algorithm based on the continuous representation of Gauss sums, generalizable to orders j > 2. Such an algorithm allows one, for the first time, to find all the factors of a number N in a single run without precalculating the ratio N/l, where l are all the possible trial factors. Continuous truncated exponential sums turn out to be a powerful tool for distinguishing factors from non-factors (we also suggest, with regard to this topic, to read an interesting paper by S. Wolk et al. also published in this issue [Wolk, Feiler, Schleich, J. Mod. Opt. in press]) and factorizing different numbers at the same time. We will also describe two possible M-path optical interferometers, which can be used to experimentally realize this algorithm: a liquid crystal grating and a generalized symmetric Michelson interferometer. [ABSTRACT FROM AUTHOR]

The effectiveness of different concrete and pictorial models on students' understanding of the part-whole construct for fractions was investigated. Using interview data from fourth and fifth grade students from three different districts that adopted the Mathematics Trailblazers series, authors identified strengths and limitations of models used. Pattern blocks had limited value in aiding students' construction of mental images for the part-whole model as well as limited value in building meaning for adding and subtracting fractions. A paper fraction chart based on a paper folding model supported students' ability to order fractions with same numerators but was less useful in helping students on estimation tasks. The dot paper model and chips did not support fifth grade students' initial understanding of the algorithm. [ABSTRACT FROM AUTHOR]

In western Europe, a sophisticated banking system for the purposes of international trade had evolved by the end of the thirteenth century. It was based upon the 'bill of exchange', which enabled an exporter of goods to receive payment in his own currency, by means of a balancing payment made in the currency of the importer. This paper discusses the arithmetical tools that were available for use in accounting for transactions made in different currencies. It is argued that algorithmic methods based on the Hindu-Arabic numerals were used at the higher levels of banking, in order to prepare tables of foreign exchange such as those collected by the Florentine banker, Francesco Pegolotti. On the other hand, the clerks who were responsible for routine book-keeping would have used a simple abacus and counters, and recorded their transactions in Roman numerals. The paper is based on a talk given to the BSHM at Gresham College on 25 April 2008. [ABSTRACT FROM AUTHOR]

The k-out-of-n system structure is a very popular type of redundancy in fault-tolerant systems, with wide applications in both industrial and military systems. In this paper, the modeling, application and reliability evaluation of k-out-of-n systems are studied for the case where the components and the system have multiple performance levels. A multi-state k-out-of-n system model is proposed that allows different requirements on the number of components for different state levels, and, very importantly, more practical engineering systems can fit into this model. The multiple states in the model can be interpreted in two ways: (i) multiple levels of capacity; and (ii) multiple failure modes. Application examples of the proposed multi-state k-out-of-n system model are given under each of the interpretations. An approach is presented for efficient reliability evaluation of multi-state k-out-of-n systems with identically and independently distributed components. A recursive algorithm is proposed for reliability evaluation of multi-state k-out-of-n systems with independent components. Efficiency investigations show that both of the reliability evaluation approaches are efficient. The multi-state k-out-of-n system model with a constant k value, which is a special case of the general multi-state k-out-of-n system model, has been studied for a long time, but only on the theoretical stage. A practical application of this model is presented in this paper as well. [ABSTRACT FROM AUTHOR]

This paper characterizes the existence and form of the possible limit error signals in typical parameter-optimal iterative learning control. The set of limit errors has attracting and repelling components and the behaviour of the algorithm in the vicinity of these sets can be associated with the undesirable properties of apparent (but in fact temporary) convergence or permanent slow convergence properties in practice. The avoidance of these behaviours in practice is investigated using novel switching strategies. Deterministic strategies are analysed to prove the feasibility of the concept by proving that each of a number of such strategies is guaranteed to produce global convergence of errors to zero independent of the details of plant dynamics. For practical applications a random switching strategy is proposed to replace these approaches and shown, by example, to produce substantial potential improvements when compared with the non-switching case. The work described in this paper is covered by pending patent applications in the UK and elsewhere. [ABSTRACT FROM AUTHOR]

Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches are specially suited to learn from data by representing knowledge as density functions. In this paper, we choose Gaussian mixture models (GMMs) to represent densities, as they possess great flexibility to adequate to a wide class of problems. The classical estimation approach for GMMs corresponds to the iterative algorithm of expectation maximization (EM). This approach, however, does not scale properly to meet the high demanding processing requirements of large databases. In this paper we introduce an EM-based algorithm, that solves the scalability problem. Our approach is based on the concept of data condensation which, in addition to substantially diminishing the computational load, provides sound starting values that allow the algorithm to reach convergence faster. We also focus on the model selection problem. We test our algorithm using synthetic and real databases, and find several advantages, when compared to other standard existing procedures. [ABSTRACT FROM AUTHOR]

The objective of this paper is to find an adaptive control strategy which would enable us to estimate the parameters of the ARMAX model as accurately as possible, along with consuming less controlling energy, while keeping the output of the system below a specified level of variability. Using the self-tuning tracker, this paper establishes global convergence of a stochastic adaptive control algorithm for discrete linear system, and the adaptive controller may converge to the one-step-ahead optimal controller at the same time. [ABSTRACT FROM AUTHOR]

The goal of this paper is to further the investigation begun in Chalmers and Prophet, Numer. Funct. Anal. Optimiz. 1997; 18:507–520. With the benefit of nearly 10 years of work, we begin by indicating how several proofs from Chalmers and Prophet, Numer. Funct. Anal. Optimiz. 1997; 18:507–520, can be substantially improved. We show that the problem of preserving k-convexity onto Πn is one part of a larger shape-preserving problem (multiconvex preservation) relative to Πn, and we completely solve this expanded problem. And finally, we demonstrate that multiconvex preserving projections constructed in this paper are in fact of minimal operator norm in a large class of Banach spaces. [ABSTRACT FROM AUTHOR]

This paper addresses the reverse 1-center problem on a weighted tree. Here, a facility has already been located in a predetermined node of the tree network and we want to improve the 1-center objective value at that node as efficiently as possible within a given budget. For solving this problem under uniform linear cost functions, we develop a combinatorial algorithm with running time, whereis the number of vertices of the tree. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new simultaneous iterative algorithm for solving the split common fixed point problem of directed operators. Inspired by the idea of cyclic iterative algorithm, we also introduce two iterative algorithms which combine the process of cyclic and simultaneous together. Under mild assumptions, we prove convergence of the proposed iterative sequences. As applications, we obtain several iteration schemes to solve the inverse problem of multiple-sets split feasibility problem. Numerical experiments are presented to confirm the efficiency of the proposed iterative algorithms. [ABSTRACT FROM AUTHOR]

In this paper we examine some of the mathematical properties of Everett's graph theoretic blocking procedures. In particular we discuss how k-plexes are blocked by Everett's algorithm. We also give a mathematical definition and algorithm f or finding a class of actors identified as important in Everett's paper. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new powerful scientific paradigm to understand natural and cultural processes. This new paradigm is based on two fundamental keywords: Data, as representative sample of the process we need to analyze, and Artificial Adaptive Systems, as a new mathematical technique able to make explicit the nonlinearity embedded in the process. We will try to make explicit these concepts analyzing how the distribution of events into the physical space may reveal the hidden logic connecting these events together. [ABSTRACT FROM PUBLISHER]

In this paper, a new hybrid method based on teacher learning algorithm (TLA) and artificial neural network (ANN) is proposed to develop an accurate model to investigate short-term load forecasting more precisely. In contrast to the other evolutionary-based training techniques, the proposed method utilises both the ability of ANNs to generate a non-linear mapping among different complex data as well as the powerful ability of TLA for global search and exploration. In addition, in an attempt to choose the most satisfying features from the set of input variables, a novel feature-selection approach based on fuzzy clustering and fuzzy set theory is proposed and utilised sufficiently. In order to improve the overall performance of TLA for optimisation applications, a new modification phase is proposed to increase the ability of the algorithm to explore the entire search space globally. The simulation results show the feasibility and the superiority of the proposed hybrid method over the other well-known methods in the area. [ABSTRACT FROM AUTHOR]

This paper presents a new heuristic algorithm for solving the single-container loading problem. The algorithm is to formalize human experience formed in the last 1000 years by modern mathematical tools, and to refine it by a new observation. Its key issue is the cavity degree of an action that packs an item into a layer of the container, such that the item is packed as compactly as possible to other items already packed in the same layer. For the 1500 well-known benchmark problems from Bischoff, Ratcliff, and Davies, the new algorithm achieves an average container volume utilization of 89.59% with a reasonable average computing time of 27.78 min. This improves the current best utilization record reported in the literature considerably by 0.62%. Of those 1500, for the 800 strongly heterogeneous problems especially, where other algorithms usually achieved lower utilizations, it achieves an average volume utilization of 89.77%, which breaks the current best record markedly by 2.08%. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate decision making problems with interval-valued intuitionistic fuzzy preference information, in which the preferences provided by the decision maker over alternatives are incomplete or uncertain. We define some new preference relations, including additive consistent incomplete interval-valued intuitionistic fuzzy preference relation, multiplicative consistent incomplete interval-valued intuitionistic fuzzy preference relation and acceptable incomplete interval-valued intuitionistic fuzzy preference relation. Based on the arithmetic average and the geometric mean, respectively, we give two procedures for extending the acceptable incomplete interval-valued intuitionistic fuzzy preference relations to the complete interval-valued intuitionistic fuzzy preference relations. Then, by using the interval-valued intuitionistic fuzzy averaging operator or the interval-valued intuitionistic fuzzy geometric operator, an approach is given to decision making based on the incomplete interval-valued intuitionistic fuzzy preference relation, and the developed approach is applied to a practical problem. It is worth pointing out that if the interval-valued intuitionistic fuzzy preference relation is reduced to the real-valued intuitionistic fuzzy preference relation, then all the above results are also reduced to the counterparts, which can be applied to solve the decision making problems with incomplete intuitionistic fuzzy preference information. [ABSTRACT FROM AUTHOR]

In this paper, we study algorithms for tiling problems. We show that the conditions (T1) and (T2) of Coven and Meyerowitz [E. Coven and A. Meyerowitz, Tiling the integers with translates of one finite set, J. Algebra 212(1) (1999), pp. 161-174], conjectured to be necessary and sufficient for a finite set A to tile the integers, can be checked in time polynomial in diam (A). We also give heuristic algorithms to find all non-periodic tilings of a cyclic group N. In particular, we carry out a full classification of all non-periodic tilings of 144. [ABSTRACT FROM AUTHOR]

This paper investigates a multi-resolution digital Earth model called PYXIS, which was developed by PYXIS Innovation Inc. The PYXIS hexagonal grids employ an efficient hierarchical labeling scheme for addressing pixels. We provide a recursive definition of the PYXIS grids, a systematic approach to the labeling, an algorithm to add PYXIS labels, and a discussion of the discrete Fourier transform on PYXIS grids. [ABSTRACT FROM AUTHOR]

In this paper we investigate the structure of Euclidean rhythms and show that a Euclidean rhythm is formed of a pattern, called the main pattern, repeated a certain number of times, followed possibly by one extra pattern, the tail pattern. We thoroughly study the recursive nature of Euclidean rhythms when generated by Bjorklund's algorithm, one of the many algorithms that generate Euclidean rhythms. We make connections between Euclidean rhythms and Bezout's theorem. We also prove that the decomposition obtained is minimal. [ABSTRACT FROM AUTHOR]

In this paper, a novel state-based dynamic multi-alphabet arithmetic coding algorithm which adapts efficiently to the locally occurring symbol statistics is presented. The proposed coding algorithm is applicable to sources such as raw images or transformed images that locally produce a smaller set of symbols from a large alphabet, or any other source characterized by very large alphabet size and highly skewed distributions. The performance of the proposed algorithm is compared with two standard entropy coding schemes, CA-2D-VLC and CABAC, that have recently appeared in the literature, in terms of compression ratio, peak signal-to-noise ratio and subjective quality. The simulation results obtained are encouraging, paving the way for further research and hardware implementation of this algorithm. It is found that the proposed algorithm achieves an increase in the compression ratio of about 45% over the compared standards for the similar peak signal-to-noise ratio and subjective quality. [ABSTRACT FROM AUTHOR]

Count data with excess zeros often occurs in areas such as public health, epidemiology, psychology, sociology, engineering, and agriculture. Zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression are useful for modeling such data, but because of hierarchical study design or the data collection procedure, zero-inflation and correlation may occur simultaneously. To overcome these challenges ZIP or ZINB may still be used. In this paper, multilevel ZINB regression is used to overcome these problems. The method of parameter estimation is an expectation-maximization algorithm in conjunction with the penalized likelihood and restricted maximum likelihood estimates for variance components. Alternative modeling strategies, namely the ZIP distribution are also considered. An application of the proposed model is shown on decayed, missing, and filled teeth of children aged 12 years old. [ABSTRACT FROM AUTHOR]

A continuous location problem in which a firm wants to set up a single new facility in a competitive environment is considered. Other facilities offering the same product or service already exist in the area. Both the location and the quality of the new facility are to be found so as to maximize the profit obtained by the firm. This is a hard-to-solve global optimization problem. An evolutionary algorithm called Universal Evolutionary Global Optimizer (UEGO) seems to be the best procedure to cope with it, but the algorithm needs several hours of CPU time for solving large instances. In this paper, four parallelizations of UEGO are presented. They all are coarse-grain methods which differ in their migratory policies. A computational study is carried out to compare the performance of the parallel algorithms. The results show that one of the parallelizations always gives the best objective function value and has an almost linear speed-up for up to 16 processing elements for large instances. [ABSTRACT FROM AUTHOR]

In this paper we present a new class of memory gradient methods for unconstrained optimization problems and develop some useful global convergence properties under some mild conditions. In the new algorithms, trust region approach is used to guarantee the global convergence. Numerical results show that some memory gradient methods are stable and efficient in practical computation. In particular, some memory gradient methods can be reduced to the BB method in some special cases. [ABSTRACT FROM AUTHOR]

An extended Heaviside calculus proposed by Péraire in a recent paper is similar to a generalization of the Laplace transform proposed by the present author. This similarity will be illustrated by analysis of an example supplied by Péraire. [ABSTRACT FROM AUTHOR]

Kolodko and Schoenmakers (2006) and Bender and Schoenmakers (2006) introduced a policy iteration that allows the achievement of a tight lower approximations of the price for early exercise options via a nested Monte Carlo simulation in a Markovian setting. In this paper we enhance the algorithm by a scenario selection method. It is demonstrated by numerical examples that the scenario selection can significantly reduce the number of inner simulations actually performed, and thus can greatly speed up the method (by up to a factor of 15 in some examples). Moreover, it is shown that the modified algorithm retains the desirable properties of the original, such as the monotone improvement property, termination after a finite number of iteration steps, and numerical stability. [ABSTRACT FROM AUTHOR]

Direct search optimization algorithms are becoming an important alternative to well-established gradient based methods. Due to the fact that a single cost function evaluation may take a substantial amount of time, optimization can be a lengthy process. In order to shorten the run time one often resorts to parallel algorithms. Asynchronous algorithms are particularly efficient since they have no synchronization points. This paper is an attempt to establish a convergence theory for a class of such parallel direct search algorithms. The notion of a search direction generator (SDG) is introduced. An algorithmic framework for parallel distributed optimization methods based on SDGs is presented along with the corresponding convergence theory. The theory almost completely decouples the stepsize control from the sufficient descent requirement, which is necessary for the finite termination of the algorithm's inner loop. The proposed framework has several attributes considered very favourable in loosely coupled parallel systems (e.g. clusters of workstations), such as fault tolerance and scalability. The framework is illustrated by optimizing a set of test problems on a cluster of workstations. In all tested cases, a speedup was obtained that increased with the increasing number of workstations. Fault tolerance and scalability of the framework were also demonstrated by removing and adding workstations to the cluster while an optimization run was in progress. [ABSTRACT FROM AUTHOR]

Modelling of the growth of a decagonal Cartwheel-type tiling is not described well enough by the well known matching rules of Penrose tiles. This paper presents a deterministic algorithm which allows the calculation of a perfect Cartwheel-type tiling by the successive transfer of recursive values out of each cluster cell, Q, to its neighbour cells. [ABSTRACT FROM AUTHOR]

The aim of our paper is to provide a new class of five-phase anti-lock brake algorithms (that use wheel deceleration logic-based switching) and a simple mathematical background that explains their behavior. First, we completely characterize the conditions required for our algorithm to work. Next, we explain how to compute analytically an approximation of the Poincaré map of the system (without using numerical integration) and show how to calibrate the algorithm’s parameters to obtain the most efficient limit cycle. [ABSTRACT FROM AUTHOR]

In this paper, by using a so-called fixed steplength strategy, we propose a BFGS method without use of line searches for unconstrained optimization. Under mild conditions, we show the global and superlinear convergence of the proposed method even when the objective function is nonconvex. [ABSTRACT FROM AUTHOR]

In this paper, efficient reinforcement learning through dynamic-form symbiotic evolution (DSE) is proposed for solving nonlinear control problems. Compared with traditional symbiotic evolution, DSE uses the sequential search-based dynamic evolution (SSDE) method to generate an initial population and to determine dynamic mutation points. Therefore, better chromosomes will be initially generated while better mutation points will be determined for performing dynamic mutation. The proposed DSE design method was applied to different control systems, including the cart-pole balancing system and the water bath temperature control system, and control problems were simulated on these systems. The proposed DSE method was verified to be efficient and superior for solving these control problems and from comparisons with some traditional genetic algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with a discrete monotone iterative algorithm for solving a non-linear singularly perturbed parabolic reaction–diffusion problem. The monotone iterative method, which is based on a weighted average difference scheme, is applied to computing a non-linear difference scheme obtained after discretization of the continuous problem. This method solves only linear discrete systems at each iterative step of the iterative process. The uniform convergence of the method is established and numerical experiments are presented. [ABSTRACT FROM AUTHOR]

Let G =( V , E ) be a graph with vertex set V of size n and edge set E of size m . A vertex v ? V is called a hinge vertex if there exist two vertices in V \{ v } such that their distance becomes longer when v is removed. In this paper, we present a distributed algorithm that finds all hinge vertices on an arbitrary graph. The proposed algorithm works for named static asynchronous networks and achieves O ( n 2 ) time complexity and O ( m ) message complexity. In particular, the total messages exchanged during the algorithm are at most 2 m (log n + n log n +1) bits. [ABSTRACT FROM AUTHOR]

Although there are many text classification techniques depending on the vector space, it is difficult to detect the meaning related to the user’s intention (complaint, encouragement, request, invitation, etc.). The approach be discussed in this paper is very useful for understanding focus points in conversation. We present a technique for determining the speaker’s intention for sentences in conversation. Intention association expressions are introduced, and formal descriptions with weights are defined using these expressions to construct an intention classification. A deterministic multi-attribute pattern-matching algorithm is used to determine the intention class efficiently. In simulation results for 681 email messages of 5859 sentences, the multi-attribute pattern-matching algorithm is about 44.5 times faster than the Aho and Corasick method. The precision and recall of intention classification of sentences are 91% and 95%, respectively. The precision and recall of extraction of unnecessary sentences are 98% and 96%, respectively. The precision and recall of the classification of each email are 88% and 89%, respectively. [ABSTRACT FROM AUTHOR]

Fuzzy classification systems are important applications of the fuzzy set theory. In order to design a fuzzy classification system, it is an important task to construct the membership function of each attribute and generate fuzzy rules that are suitable for handling a specific classification problem. In this paper, we propose a new method to construct the membership function of each attribute and generate weighted fuzzy rules from training instances for handling fuzzy classification problems. The proposed method can construct membership functions and generate weighted fuzzy rules without any human experts' intervention. It can get a higher average classification accuracy rate and generate fewer fuzzy rules than the existing methods. [ABSTRACT FROM AUTHOR]

The p -centre problem is to locate p facilities on a network so as to minimize the largest distance from a demand point to its nearest facility. The problem is NP-complete for an arbitrary network. In this paper, genetic algorithms (GAs) to solve this problem are developed via two different representations. The nodes are taken as weighted, and the demand points are assumed to coincide with the nodes. Computational results obtained from the proposed GAs for different network sizes and different values of p are presented and compared for two different representations. [ABSTRACT FROM AUTHOR]

In this paper, we formulate the optimal supervisory control problem (OSCP) under partial observation and propose an algorithm to design the optimal nonblocking supervisor (ONS). In addition, we introduce an extended net cost including the observation cost of the events which is taken into account for the cost of setting up a mechanism to sense the events and solve the OSCP with the extended net cost. [ABSTRACT FROM AUTHOR]

The Rayleigh conjecture on the representation of the scattered field in the exterior of an obstacle D is widely used in applications. However, this conjecture is false for some obstacles. A.G.R. introduced the modified Rayleigh conjecture (MRC), and in this paper we present successful numerical algorithms based on the MRC for various 2D and 3D obstacle scattering problems. The 3D obstacles include a cube and an ellipsoid. The MRC method is easy to implement for both simple and complex geometries. It is shown to be a viable alternative for other obstacle scattering methods. [ABSTRACT FROM AUTHOR]

A recent development in data compression area is Burrows-Wheeler Compression algorithm (BWCA). Introduced by Burrows and Wheeler, the BWCA achieves compression ratio closer to the best compression techniques, such as partial pattern matching (PPM) techniques, but with a faster execution speed. In this paper, we analyze the combinatorial properties of the Burrows-Wheeler transformation (BWT), which is a block-sorting transformation and an essential part of the BWCA, introduce a new transformation, and delineate the new transformation with the BWT based on the multiset permutations. * Some sections of this work have been presented at two different meetings of the IEEE Data Compression Conference, DCC-1998 and DCC-2002. ‡ E-mail: arnavut@cs.fredonia.edu [ABSTRACT FROM AUTHOR]

In the paper, a new trust region algorithm is given for nonlinear inequality constrained optimization problems. Motivated by a dual problem introduced by Han and Mangasarian [Han, S. P., Mangasarian, O. L. (1983). A dual differentiable exact penalty function. Math. Programming 25:293-306], which is a nonnegatively constrained maximization problem, we construct a trust region algorithm for solving the dual problem. At each iteration, we only need to minimize a quadratic subproblem with simple bound constraints. Under the condition that the iterate sequence generated by the algorithm is contained in some bounded closed set, any accumulation point of the sequence is a Karush- Kuhn-Tucker point of the original problem. [ABSTRACT FROM AUTHOR]

In the context of pavement evaluation, backcalculation is the process of estimation of elastic properties of pavement layers using measured structural responses. A number of backcalculation programs are available for estimating effective pavement layer moduli from surface deflections. Different optimization techniques have been used in the development of these backcalculation models. Genetic algorithm (GA) has been used successfully in the recent past for backcalculation of pavement layer moduli. Selection of appropriate GA parameters is important for the efficient performance of the GA-based algorithm. However, there are no general guidelines available at present for the selection of GA parameters. This paper presents a study conducted for the selection of optimal GA parameters to be adopted for backcalculation of pavement layer moduli. The parameters have been selected on the basis of the level of accuracy desired and the corresponding computational effort (CE). The performance of the GA-based program with the selected parameters was evaluated using some hypothetical and field problems. [ABSTRACT FROM AUTHOR]