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The celebrated simplicity of the EM algorithm is somewhat lost in its common use for generalized linear mixed models (GLMMs) because of its analytically intractable E-step. A natural and typical strategy in practice is to implement the E-step via Monte Carlo by drawing the unobserved random effects from their conditional distribution as specified by the E-step. In this paper, we show that further augmenting the missing data (e.g., the random effects) used by the M-step leads to a quite attractive and general slice sampler for implementing the Monte Carlo E-step. The slice sampler scheme is straightforward to implement, and it is neither restricted to the particular choice of the link function (e.g., probit) nor to the distribution of the random effects (e.g., normal). We apply this scheme to the standard EM algorithm as well as to an alternative EM algorithm which treats the variance-standardized random effects, rather than the random effects themselves, as missing data. The alternative EM algorithm does not only have faster convergence, but also leads to generalized linear model-like variance estimation, because it converts the random-effect standard deviations into linear regression parameters. Using the well-known salamander mating problem, we compare these two algorithms with each other, as well as with a variety of methods given in the literature in terms of the resulting point and interval estimates. [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]

Landscape paradigm is ubiquitous in physics and other natural sciences, but it has to be supplemented with both quantitative and qualitatively meaningful tools for analyzing the topography of a given landscape. We here consider dynamic explorations of the relief and introduce as basic topographic features “wells of duration T and altitude y.” We determine an intrinsic exploration mechanism governing the evolutions from an initial state in the well up to its rim in a prescribed time, whose finite-difference approximations on finite grids yield a constructive algorithm for determining the wells. Our main results are thus (i) a quantitative characterization of landscape topography rooted in a dynamic exploration of the landscape, (ii) an alternative to stochastic gradient dynamics for performing such an exploration, (iii) a constructive access to the wells, and (iv) the determination of some bare dynamic features inherent to the landscape. The mathematical tools used here are not familiar in physics: They come from set-valued analysis (differential calculus of set-valued maps and differential inclusions) and viability theory (capture basins of targets under evolutionary systems) that have been developed during the last two decades; we therefore propose a minimal Appendix exposing them at the end of this paper to bridge the possible gap. [ABSTRACT FROM AUTHOR]

The brain has evolved to enable organisms to survive in a complicated and dynamic world. Its operation is based upon a priori models of the environment which are adapted, during learning, in response to new and changing stimuli. The same qualities that make biological learning mechanisms ideal for organisms make their underlying mathematical algorithms ideal for certain technological applications, especially those concerned with understanding the physical processes giving rise to complicated data sets. In this paper, we offer a mathematical model for the underlying mechanisms of biological learning, and we show how this mathematical approach to learning can yield a solution to the problem of imaging time-varying objects from X-ray computed tomographic (CT) data. This problem relates to several practical aspects of CT imaging including the correction of motion artifacts caused by patient movement or breathing. [ABSTRACT FROM AUTHOR]

This paper proposes a procedure to design fault tolerant algorithms for the R-Mesh and some of its restrictive variations. This procedure first identifies a healthy sub-mesh from a faulty model using the bypass and removal fault model. Then it uses scalable algorithms to simulate the larger faulty model on the resulting healthy sub-mesh. The algorithms for the bypass model tolerates n faults in an n×n R-Mesh (LR-Mesh) and runs in O(T log n) (O(T)) time, where T is the execution time on the original mesh without faults. For the removal model, we design fault tolerant algorithms for some interesting variations of the R-Mesh, specifically, the NXR-Mesh and the NXLR-Mesh. We propose the first scaling simulations for these models and present a simulation of the R-Mesh on the NXR-Mesh. The results of this paper enable us to consider certain reconfigurable models in a more practical environment than previously allowed. [ABSTRACT FROM AUTHOR]

M. Lerman and J. Scmerl specified some sufficient conditions for computable models of countably categorical arithmetical theories to exist. More precisely, it was shown that if $T$ is a countably categorical arithmetical theory, and the set of its sentences beginning with an existential quantifier and having at most $n+1$ alternations of quantifiers is $\backslash Sigma\_\{n+1\}^0$ for any $n$, then $T$ has a computable model. J. Night improved this result by allowing certain uniformity and omitting the requirement that $T$ is arithmetical. However, all of the known examples of theories of $\backslash aleph\_0$-categorical computable models had low level of algorithmic complexity, and whether there are theories that would satisfy the above conditions for sufficiently large $n$ was unknown. This paper will include such examples. [ABSTRACT FROM AUTHOR]

The geometric processing of remote sensing images becomes a key issue in multi-source data integration, management and analysis for many geomatic applications. This paper first reviews the source of geometric distortions, compares the different mathematical models being currently used for geometric distortion modelling, details the algorithms, methods and processing steps and finally tracks the error propagation from the input to the final output data. [ABSTRACT FROM AUTHOR]

This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic and regularised orthogonal least squares. The proposed algorithm aims to achieve maximised model robustness via two effective and complementary approaches, parameter regularisation via ridge regression and model optimal generalisation structure selection. The major contributions are to derive the PRESS error in a regularised orthogonal weight model, develop an efficient recursive computation formula for PRESS errors in the regularised orthogonal least squares forward regression framework and hence construct a model with a good generalisation property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated model construction procedure without resort to any other validation data set for model evaluation. [ABSTRACT FROM AUTHOR]

The paper is a generalization of [4], [5] for arbitrary distributions of coefficients. It is supposed that the coefficients of the objective function and the constraint of the knapsack problem are independent identically distributed random variables having a density with support [0, 1], and the right-hand side of the constraint is proportional to the number of variables, i. e. b = λn. We establish a bound on λ (in terms of the given density and a parameter t > 0) ensuring that both the primal and the dual greedy algorithms have an asymptotic tolerance t. [ABSTRACT FROM AUTHOR]

A successive continuation method for locating connecting orbits in parametrized systems of autonomous ODEs was considered in [Numer. Algorithms, 14 (1997), pp. 103­124]. In this paper we present an improved algorithm for locating and continuing connecting orbits, which includes a new algorithm for the continuation of invariant subspaces. The latter algorithm is of independent interest and can be used in contexts different than the present one. [ABSTRACT FROM AUTHOR]

This paper presents the results of an empirical study of the effects of coefficient correlation structure and constraint slackness settings on the performance of solution procedures on synthetic two-dimensional knapsack problems (2KP). The population correlation structure among 2KP coefficients, the level of constraint slackness, and the type of correlation (product moment or rank) are varied in this study. Representative branch-and-bound and heuristic solution procedures are used to investigate the influence of these problem parameters on solution procedure performance. Population correlation structure, and in particular the interconstraint component of the correlation structure, is found to be a significant factor influencing the performance of both the algorithm and the heuristic. In addition, the interaction between constraint slackness and population correlation structure is found to influence solution procedure performance. [ABSTRACT FROM AUTHOR]

This paper describes three approaches to assigning tasks to earth observing satellites (EOS). A fast and simple priority dispatch method is described and shown to produce acceptable schedules most of the time. A look ahead algorithm is then introduced that outperforms the dispatcher by about 12% with only a small increase in run time. These algorithms set the stage for the introduction of a genetic algorithm that uses job permutations as the population. The genetic approach presented here is novel in that it uses two additional binary variables, one to allow the dispatcher to occasionally skip a job in the queue and another to allow the dispatcher to occasionally allocate the worst position to the job. These variables are included in the recombination step in a natural way. The resulting schedules improve on the look ahead by as much as 15% at times and 3% on average. We define and use the "window-constrained packing" problem to model the bare bones of the EOS scheduling problem. [ABSTRACT FROM AUTHOR]

Routing problems appear frequently when dealing with the operation of communication or transportation networks. Among them, the message routing problem plays a determinant role in the optimization of network performance. Much of the motivation for this work comes from this problem which is shown to belong to the class of nonlinear convex multicommodity flow problems. This paper emphasizes the message routing problem in data networks, but it includes a broader literature overview of convex multicommodity flow problems. We present and discuss the main solution techniques proposed for solving this class of large-scale convex optimization problems. We conduct some numerical experiments on the message routing problem with four different techniques. [ABSTRACT FROM AUTHOR]

The problem in this paper is motivated by physical problems concerned with the case when a class of continuous and equimeasurable densities of a string is given then how to find minimal frequencies among these given densities, that is, what kind of densities minimize the frequencies. By taking Dirichlet eigenvalues into account, given a certain weight function ω, we will show the minimizer of the mth eigenvalue is the m-degree continuous symmetrical decreasing rearrangement of ω. The main result of this paper can be viewed as complementary to Schwarz’s work (Schwarz in J. Math. Mech. 10:401-422, 1961). [ABSTRACT FROM AUTHOR]

In a discrete-event simulation, the information related to the events scheduled to occur in the future is kept in a data structure called the future event set (FES). In this paper, we study the interaction hold model a popular stochastic model for FES performance analysis, corresponding to the superposition of a (fixed) number of renewal processes. The general state-space Markov chain formed by the discrete-time process that keeps track, at event times, of the residual lifetimes is shown here to be recurrent in the sense of Harris, and its stationary distribution is obtained. Linked lists and indexed lists, two popular FESs, are investigated using this model. For the interaction hold model, we make rigorous certain published results as well as introduce new ones. For example, we derive the distribution of the relative position of the event to be inserted in the data structure. In the exponential case, our analytic and empirical results confirm that when events with relatively short lifetimes often get regenerated upon their occurrence, it is better to scan a list (or sublist) from its head rather than tail. In the same context and for indexed lists with sublists with constant sizes, our results suggest that subsequent sublists should be of larger sizes, i.e., the first sublist should contain the smallest number of records, the second sublist the second smallest number of records, etc. [ABSTRACT FROM AUTHOR]

Fast Marching Methods are numerical schemes for computing solutions to the nonlinear Eikonal equation and related static. Hamilton­Jacobi equations. Based on entropy-satisfying upwind schemes and fast sorting techniques, they yield consistent, accurate, and highly efficient algorithms. They are optimal in the sense that the computational complexity of the algorithms is O(N log N), where N is the total number of points in the domain. The schemes are of use in a variety of applications, including problems in shape offsetting, computing distance from compiles curves and surfaces, shape-from-shading, photolithographic development, computing first arrivals in seismic travel times, construction of shortest geodesies on surfaces, optimal path planning around obstacles, and visibility and reflection calculations. In this paper, were review the development of these techniques, including the theoretical and numerical underpinnings; provide details of the computational schemes, including higher order versions; and demonstrate the techniques in a collection of different areas. [ABSTRACT FROM AUTHOR]

Identifying communities or clusters in networked systems has received much attention across the physical and social sciences. Most of this work focuses on single layer or one-mode networks, including social networks between people or hyperlinks between websites. Multilayer or multi-mode networks, such as affiliation networks linking people to organizations, receive much less attention in this literature. Common strategies for discovering the community structure of multi-mode networks identify the communities of each mode simultaneously. Here I show that this combined approach is ineffective at discovering community structures when there are an unequal number of communities between the modes of a multi-mode network. I propose a dual-projection alternative for detecting communities in multi-mode networks that overcomes this shortcoming. The evaluation of synthetic networks with known community structures reveals that the dual-projection approach outperforms the combined approach when there are a different number of communities in the various modes. At the same time, results show that the dual-projection approach is as effective as the combined strategy when the number of communities is the same between the modes. [ABSTRACT FROM AUTHOR]

The kidney exchange programs bring new insights in the field of organ transplantation. They make the previously not allowed surgery of incompatible patient-donor pairs easier to be performed on a large scale. Mathematically, the kidney exchange is an optimization problem for the number of possible exchanges among the incompatible pairs in a given pool. Also, the optimization modeling should consider the expected quality-adjusted life of transplant candidates and the shortage of computational and operational hospital resources. In this article, we introduce a bio-inspired stochastic-based Ant Lion Optimization, ALO, algorithm to the kidney exchange space to maximize the number of feasible cycles and chains among the pool pairs. Ant Lion Optimizer-based program achieves comparable kidney exchange results to the deterministic-based approaches like integer programming. Also, ALO outperforms other stochastic-based methods such as Genetic Algorithm in terms of the efficient usage of computational resources and the quantity of resulting exchanges. Ant Lion Optimization algorithm can be adopted easily for on-line exchanges and the integration of weights for hard-to-match patients, which will improve the future decisions of kidney exchange programs. A reference implementation for ALO algorithm for kidney exchanges is written in MATLAB and is GPL licensed. It is available as free open-source software from: . [ABSTRACT FROM AUTHOR]

Identifying hierarchies and rankings of nodes in directed graphs is fundamental in many applications such as social network analysis, biology, economics, and finance. A recently proposed method identifies the hierarchy by finding the ordered partition of nodes which minimises a score function, termed agony. This function penalises the links violating the hierarchy in a way depending on the strength of the violation. To investigate the resolution of ranking hierarchies we introduce an ensemble of random graphs, the Ranked Stochastic Block Model. We find that agony may fail to identify hierarchies when the structure is not strong enough and the size of the classes is small with respect to the whole network. We analytically characterise the resolution threshold and we show that an iterated version of agony can partly overcome this resolution limit. [ABSTRACT FROM AUTHOR]

For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide free services that are valued by billions of people globally. [ABSTRACT FROM AUTHOR]

The ability to predict what courses a student may enroll in the coming semester plays a pivotal role in the allocation of learning resources, which is a hot topic in the domain of educational data mining. In this study, we propose an innovative approach to characterize students’ cross-college course enrollments by leveraging a novel contextual graph. Specifically, different kinds of variables, such as students, courses, colleges and diplomas, as well as various types of variable relations, are utilized to depict the context of each variable, and then a representation learning algorithm node2vec is applied to extracting sophisticated graph-based features for the enrollment analysis. In this manner, the relations between any pair of variables can be measured quantitatively, which enables the variable type to transform from nominal to ratio. These graph-based features are examined by the random forest algorithm, and experiments on 24,663 students, 1,674 courses and 417,590 enrollment records demonstrate that the contextual graph can successfully improve analyzing the cross-college course enrollments, where three of the graph-based features have significantly stronger impacts on prediction accuracy than the others. Besides, the empirical results also indicate that the student’s course preference is the most important factor in predicting future course enrollments, which is consistent to the previous studies that acknowledge the course interest is a key point for course recommendations. [ABSTRACT FROM AUTHOR]

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1–4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast. [ABSTRACT FROM AUTHOR]

Network propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib. [ABSTRACT FROM AUTHOR]

Zero-sum sequential games where both control variables are stopping times are considered. Two game problems are dealt with: (G[sub 1]) is a problem in which the players are allowed the possibility of not stopping the game, and in the other (G[sub 2]) they are obliged to stop the observed process at some finite (but not preassigned) time. The problem (G[sub 1]) is well known. In the present paper we mainly investigate (G[sub 2]) as compared with (G[sub 1]). We give sufficient conditions for the game problem (G[sub 2]) to have a value which is consequently equal to that of (G[sub 1]) and, in parallel with it, present the constructive algorithm of the value in the natural form. The saddle point in each problem is found under a certain condition. The monotone case and the Markov case are finally investigated as the special cases. [ABSTRACT FROM AUTHOR]

The Steiner Problem in graphs is the problem of finding a set of edges (arcs) with minimum total weight which connects a given set of nodes in an edge-weighted graph (directed or undirected). This paper develops models for the directed Steiner tree problem on graphs. New and old models are examined in terms of their amenability to solution schemes based on Lagrangean relaxation. As a result, three alogrithms are presented and their performance compared on a number of problems originally tested by Beasley (1984, 1987) in the case of undirected graphs. [ABSTRACT FROM AUTHOR]

In this paper, we describe the architecture of an object-oriented scheduling system. First, a mathematical framework is presented that is based on set theory and graph theory. Then a number of basic as well as more specialized methods are defined which can be applied on the entities of any decision support system. The principal objects of a scheduling system are defined, as well as the methods specifically designed for the manipulation of the schedules. The object base design, the schedule generator design and the user interface design are then discussed in detail. [ABSTRACT FROM AUTHOR]

We present the problem of locating vehicle depots in an intercity freight transportation system with the objective of satisfying client demand for empty vehicles, while minimizing depot opening and operating costs, bidirectional client-depot transportation costs, as well as the costs of the interdepot movements necessary to counter the unbalancing of demand. We propose a class of models which has the form of a two-echelon multimode multicommodity location formulation with interdepot balancing requirements. We analyse the models and their properties to determine their underlying structure and characteristics to be used m subsequent algorithmic developments. [ABSTRACT FROM AUTHOR]

In this paper, we examine the uniqueness (discrimination power) of a newly proposed graph invariant based on the matrix defined by Randić et al. In order to do so, we use exhaustively generated graphs instead of special graph classes such as trees only. Using these graph classes allow us to generalize the findings towards complex networks as they usually do not possess any structural constraints. We obtain that the uniqueness of this newly proposed graph invariant is approximately as low as the uniqueness of the Balaban index on exhaustively generated (general) graphs. [ABSTRACT FROM AUTHOR]

How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods. [ABSTRACT FROM AUTHOR]

Research on new techniques of perforated plug silencers has been well addressed. Most researchers have explored noise reduction effects based on a pure plane wave theory. However, the maximum noise reduction of a silencer under a space constraint, which frequently occurs in engineering problems, is rarely addressed. Therefore, the optimum design of mufflers becomes an essential issue. In this paper, to save the design time during the flexible optimum process, a simplified mathematical model of a muffler is constructed with a neural network with a series of real data - input design data (muffle dimensions) and output data (theoretical sound transmission loss (STL)) were approximated by a theoretical mathematical model (TMM) in advance. To assess the optimal mufflers, the neural network model (NNM) is used as an objective function in conjunction with a genetic algorithm (GA). Moreover, the numerical cases of sound elimination with respect to various parameter sets and pure tones (500, 1000, and 2000 Hz) are exemplified and discussed. Before the GA operation is carried out, the approximation between TMM and real data is checked. In addition, both the TMM and NNM are compared. It is found that the TMM and the experimental data are in agreement. Moreover, the TMM and NNM conform. Optimal results reveal that the maximum amount of the STL can be optimally obtained at the desired frequencies. Consequently, the optimum algorithm proposed in this study can provide an efficient method to develop optimal silencers in industry. [ABSTRACT FROM AUTHOR]

One can define in a natural way irregular 1-sets on the graphs of several fractal functions, like Takagi’s function, Weierstrass-Cellerier type functions and the typical continuous function. These irregular 1-sets can be useful during the investigation of level-sets and occupation measures of these functions. For example, we see that for Takagi’s function and for certain Weierstrass-Cellerier functions the occupation measure is singular with respect to the Lebesgue measure and for almost every level the level set is finite. [ABSTRACT FROM AUTHOR]

In this work, we consider a complex flowshop scheduling problem in which both no-wait and separate setup times are considered. The optimisation criterion is the minimisation of the total completion time. We propose an effective dominance rule for the four machine case that can also be used for m machines. Five simple and fast heuristics are proposed along with two easy to code stochastic local search methods, one of them being based on Iterated Local Search (ILS). An extensive computational evaluation is carried out with two sets of 5,400 instances. All seven methods are compared to two recent algorithms. The results, confirmed by thorough statistical analyses, show that the proposed methods are more effective and efficient when compared to the best existing algorithms in the literature for the considered problem. [ABSTRACT FROM AUTHOR]

This study demonstrates the application of one-dimensional discrete wavelet transforms in the classification of T-ray pulsed signals. Fast Fourier transforms (FFTs) are used as a feature extraction tool and a Mahalanobis distance classifier is employed for classification. Soft threshold wavelet shrinkage de-noising is used and plays an important role in de-noising and reconstruction of T-ray pulsed signals. An iterative algorithm is applied to obtain three optimal frequency components and to achieve preferred classification performance. [ABSTRACT FROM AUTHOR]

A new multi-class classifier, decision tree SVM (DTSVM) which is a binary decision tree with a very simple structure is presented in this paper. In DTSVM, a problem of multi-class classification is decomposed into a series of ones of binary classification. Here, the binary decision tree is generated by using kernel clustering algorithm, and each non-leaf node represents one binary classification problem. By compared with the other multi-class classification methods based on the binary classification SVMs, the scale and the complexity of DTSVM are less, smaller number of support vectors are needed, and has faster test speed. The final simulation results confirm the feasibility and the validity of DTSVM. [ABSTRACT FROM AUTHOR]

The Multi-Tier (M-Tier) algorithm proposed in [1], has enabled an enhanced correlation between dynamic resource utilization and QoS guarantees. The algorithm favored the delay sensitive applications. Indirectly, this infers that the assignment of priorities practiced in the algorithm is static. In redesigning the algorithm, priority assignments should be able to be implemented dynamically. Thus, enabling an embedded element to provide implementation mobility. This would depend upon the current status of the classified buffers and the tolerance level of the delay sensitive traffic. Integrating control parameters to enable alternation in scheduling priorities is an option to resolve the element of partiality present in the M-Tier algorithm. Thereafter, the proposed algorithm will be denoted as the M-Tier(T) algorithm [2]. The paper proposes a detailed mathematical model for the analysis of dynamic resource reservation algorithms implemented in IP switches. The semi-markov process (SMP) approach utilized provides an accurate performance prediction and representation of resource reservation mechanisms. The algorithm correlates the average packet delay and packet loss ratio parameters to activate the dynamic resource reservation mechanism in a hierarchical manner. The developed models are validated through discrete-event simulation. The performance of the algorithm is studied in terms of average packet delay and network throughput. [ABSTRACT FROM AUTHOR]

It is to explore how the government makes use of the limited resources in the promotion of competitiveness for whole ports via grey model GM(1,1| π, r), which can conduct prediction by considering both extrapolation timing and corresponding intensity in the mean time. Therefore, for competitive advantage analyzing port in resource investment, the research shows that this model is very good, and can be effective to avert waste of resources. [ABSTRACT FROM AUTHOR]

In this paper, we consider a set of mobile nodes, each of which moves with constant velocity. We present algorithms to determine which of these mobile nodes can be captured the soonest by a set of one or more pursuers. We use a time parameterized R-tree (TPR-tree) to index these moving points. We then develop algorithms that operate on the TPR-tree to answer three query variations: (1) a single pursuer that is faster than all of the mobile nodes, (2) a single pursuer that is slower than some of the mobile nodes and (3) multiple pursuers. Experimental results show that our algorithms are simpler and faster than other approaches to solve the problem. [ABSTRACT FROM AUTHOR]

In this paper I present a proposal to obtain appropriate initial conditions while solving general equilibrium rational expectations models with the Parameterized Expectations Algorithm. The proposal is based on a log-linear approximation for the model under study, so that it can be a particular variant of the homotopy approach. The main advantages of the proposal are: (i) it guarantees the ergodicity of the initial time series used as an input to the Parameterized Expectations Algorithm; (ii) it performs well in regard to the speed of convergence when compared to some homotopy alternatives; (iii) it is easy to implement. The claimed advantages are successfully illustrated in the framework of the Cooley and Hansen (1989) model with indivisible labor and money demand motivated via a cash-in-advance constraint, as compared to a procedure based on the standard implementation of homotopy principles. [ABSTRACT FROM AUTHOR]

In addition to the common analysis of the Kauffman NK model where the value of K and the structure of interaction are given, the aim of this paper is to study what the values of these two parameters would be if they were endogenized. Thus, a model is proposed where firms and business schools coordinate to search for high peaks in their respective landscapes using evolutionary algorithms. The main result coming from the analysis of the model is that agents, using evolutionary algorithms, attempt to simplify the problems of coordination and this, over time, produces the existence in the economy of agents using many different strategies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a time-based channel reservation algorithm (TCRA) suitable for handover and call admission control procedures in future mobile satellite systems. These systems are characterized by a high rate of handover attempts which can degrade significantly their performance. Therefore, we propose TCRA, a scheme which guarantees a null handover failure probability by using a channel reservation strategy in the cells to be crossed by the user. The performance of TCRA has been compared to the guaranteed handover (GH) scheme. The TCRA reservation method has the advantage of a better channel utilization by locking the resources only for their expected time of use. A mathematical model has been developed for both schemes, and its results have been validated through simulations. Copyright © 2003 John Wiley & Sons, Ltd. [Copyright &y& Elsevier]

A new method named as Statistical Average Algorithm is developed for solving the structural dynamic compound inverse problem, which means to identify structural parameters with unknown input or to inverse input time history with unknown structural parameters. By taking the mechanical characteristic of the ground motion as an additional condition, an iterative algorithm based on the least-squares technique is developed so that the input process and structural parameters can be correctly determined using only output measurements. The procedure of the proposed method is discussed in detail, and a mathematical proof is presented as well in the appendix. A practical input situation such as earthquake, ambient vibration is considered in the numerical examples to verify the accuracy, reliability and robustness of the proposed algorithm. Considering the background of the practical application, both of the noise-free and noise-included output responses are considered in the numerical examples. In all cases, the proposed method identifies the structural parameters and reconstructs the input process rationally. [ABSTRACT FROM AUTHOR]

High dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. To address this issue different penalized regression procedures have been introduced in the litrature, but these methods cannot cope with the problem of outliers and leverage points in the heavy tailed high dimensional data. For this purppose, a new Robust Adaptive Lasso (RAL) method is proposed which is based on pearson residuals weighting scheme. The weight function determines the compatibility of each observations and downweight it if they are inconsistent with the assumed model. It is observed that RAL estimator can correctly select the covariates with non-zero coefficients and can estimate parameters, simultaneously, not only in the presence of influential observations, but also in the presence of high multicolliearity. We also discuss the model selection oracle property and the asymptotic normality of the RAL. Simulations findings and real data examples also demonstrate the better performance of the proposed penalized regression approach. [ABSTRACT FROM AUTHOR]

The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA’s results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems. [ABSTRACT FROM AUTHOR]

Gene expression time series (GETS) analysis aims to characterize sets of genes according to their longitudinal patterns of expression. Due to the large number of genes evaluated in GETS analysis, an useful strategy to summarize biological functional processes and regulatory mechanisms is through clustering of genes that present similar expression pattern over time. Traditional cluster methods usually ignore the challenges in GETS, such as the lack of data normality and small number of temporal observations. Independent Component Analysis (ICA) is a statistical procedure that uses a transformation to convert raw time series data into sets of values of independent variables, which can be used for cluster analysis to identify sets of genes with similar temporal expression patterns. ICA allows clustering small series of distribution-free data while accounting for the dependence between subsequent time-points. Using temporal simulated and real (four libraries of two pig breeds at 21, 40, 70 and 90 days of gestation) RNA-seq data set we present a methodology (ICAclust) that jointly considers independent components analysis (ICA) and a hierarchical method for clustering GETS. We compare ICAclust results with those obtained for K-means clustering. ICAclust presented, on average, an absolute gain of 5.15% over the best K-means scenario. Considering the worst scenario for K-means, the gain was of 84.85%, when compared with the best ICAclust result. For the real data set, genes were grouped into six distinct clusters with 89, 51, 153, 67, 40, and 58 genes each, respectively. In general, it can be observed that the 6 clusters presented very distinct expression patterns. Overall, the proposed two-step clustering method (ICAclust) performed well compared to K-means, a traditional method used for cluster analysis of temporal gene expression data. In ICAclust, genes with similar expression pattern over time were clustered together. [ABSTRACT FROM AUTHOR]

Background: A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model. Objective: The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study. Methods: The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index) were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units. Results: In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature. Conclusion: An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases. [ABSTRACT FROM AUTHOR]