example based learning particle swarm optimization for continuous optimization particle swarm optimization pso is a heuristic optimization technique based on swarm intelligence that is inspired by the behavior of bird flocking the canonical pso has the disadvantage of premature convergence several improved pso versions do well in keeping the diversity of the particles during the searching process but at the expense of rapid convergence this paper proposes an example based learning pso elpso to overcome these shortcomings by keeping a balance between swarm diversity and convergence speed inspired by a social phenomenon that multiple good examples can guide a crowd towards making progress elpso uses an example set of multiple global best particles to update the positions of the particles in this study the particles of the example set were selected from the best particles and updated by the better particles in the first in first out order in each iteration the particles in the example set are different and are usually of high quality in terms of the target optimization function elpso has better diversity and convergence speed than single gbest and non gbest pso algorithms which is proved by mathematical and numerical results finally computational experiments on benchmark problems show that elpso outperforms all of the tested pso algorithms in terms of both solution quality and convergence time

estimation of adjusted rate differences using additive negative binomial regression rate differences are an important effect measure in biostatistics and provide an alternative perspective to rate ratios when the data are event counts observed during an exposure period adjusted rate differences may be estimated using an identity link poisson generalised linear model also known as additive poisson regression a problem with this approach is that the assumption of equality of mean and variance rarely holds in real data which often show overdispersion an additive negative binomial model is the natural alternative to account for this however standard model fitting methods are often unable to cope with the constrained parameter space arising from the non negativity restrictions of the additive model in this paper we propose a novel solution to this problem using a variant of the expectation conditional maximisation either algorithm our method provides a reliable way to fit an additive negative binomial regression model and also permits flexible generalisations using semi parametric regression functions we illustrate the method using a placebo controlled clinical trial of fenofibrate treatment in patients with type ii diabetes where the outcome is the number of laser therapy courses administered to treat diabetic retinopathy an r package is available that implements the proposed method copyright 2016 john wiley sons ltd

degrees of freedom of two hop wireless networks everyone gets the entire cake we show that fully connected two hop wireless networks with k sources k relays and k destinations have k degrees of freedom both in the case of time varying channel coefficients and constant channel coefficients in which case the result holds for almost all values of constant channel coefficients our main contribution is a new achievability scheme which we call aligned network diagonalization this scheme allows the data streams transmitted by the sources to undergo a diagonal linear transformation from the sources to the destinations thus being received free of interference by their intended destination in addition we extend our scheme to multihop networks with fully connected hops and multihop networks with mimo nodes for which the degrees of freedom are also fully characterized

a hybrid simplex search and particle swarm optimization for unconstrained optimization this paper proposes the hybrid nm pso algorithm based on the nelder mead nm simplex search method and particle swarm optimization pso for unconstrained optimization nm pso is very easy to implement in practice since it does not require gradient computation the modification of both the nelder mead simplex search method and particle swarm optimization intends to produce faster and more accurate convergence the main purpose of the paper is to demonstrate how the standard particle swarm optimizers can be improved by incorporating a hybridization strategy in a suite of 20 test function problems taken from the literature computational results via a comprehensive experimental study preceded by the investigation of parameter selection show that the hybrid nm pso approach outperforms other three relevant search techniques i e the original nm simplex search method the original pso and the guaranteed convergence particle swarm optimization gcpso in terms of solution quality and convergence rate in a later part of the comparative experiment the nm pso algorithm is compared to various most up to date cooperative pso cpso procedures appearing in the literature the comparison report still largely favors the nm pso algorithm in the performance of accuracy robustness and function evaluation as evidenced by the overall assessment based on two kinds of computational experience the new algorithm has demonstrated to be extremely effective and efficient at locating best practice optimal solutions for unconstrained optimization

topological interference management through index coding this paper studies linear interference networks both wired and wireless with no channel state information at the transmitters except a coarse knowledge of the end to end one hop topology of the network that only allows a distinction between weak zero and significant nonzero channels and no further knowledge of the channel coefficients realizations the network capacity wired and degrees of freedom dof wireless are found to be bounded above by the capacity of an index coding problem for which the antidote graph is the complement of the given interference graph the problems are shown to be equivalent under linear solutions an interference alignment perspective is then used to translate the existing index coding solutions into the wired network capacity and wireless network dof solutions as well as to find new and unified solutions to different classes of all three problems

maximum likelihood estimation via the ecm algorithm a general framework two major reasons for the popularity of the em algorithm are that its maximum step involves only complete data maximum likelihood estimation which is often computationally simple and that its convergence is stable with each iteration increasing the likelihood when the associated complete data maximum likelihood estimation itself is complicated em is less attractive because the m step is computationally unattractive in many cases however complete data maximum likelihood estimation is relatively simple when conditional on some function of the parameters being estimated

fundamental limits of non coherent interference alignment via matroid theory we consider the problem of non coherent interference alignment in which the goal is to align the signals of multiple interfering transmitters at a single receiver where the transmitters are not aware of the channel state information we cast this problem as a problem of determining rank loss conditions for a column concatenation of full rank matrices such that each row of the composing matrices is scaled by a random coefficient we determine necessary and sufficient conditions for the design of each matrix such that the random ensemble will almost surely lose rank by a certain amount the result is proved by converting the problem to determining rank loss conditions for the union of some specific matroids and then using tools from matroid and graph theories to derive the necessary and sufficient conditions as an application we discuss how this result can be applied to the problem of topological interference management and characterize the linear symmetric degrees of freedom for a class of network topologies

iterative automated record linkage using mixture models the goal of record linkage is to link quickly and accurately records that correspond to the same person or entity whereas certain patterns of agreements and disagreements on variables are more likely among records pertaining to a single person than among records for different people the observed patterns for pairs of records can be viewed as arising from a mixture of matches and nonmatches mixture model estimates can be used to partition record pairs into two or more groups that can be labeled as probable matches links and probable nonmatches nonlinks a method is proposed and illustrated that uses marginal information in the database to select mixture models identifies sets of records for clerks to review based on the models and marginal information incorporates clerically reviewed data as they become available into estimates of model parameters and classifies pairs as links nonlinks or in need of further clerical review the procedure is illustrated with five datasets from the u s bureau

enhancing particle swarm optimization using generalized opposition based learning particle swarm optimization pso has been shown to yield good performance for solving various optimization problems however it tends to suffer from premature convergence when solving complex problems this paper presents an enhanced pso algorithm called gopso which employs generalized opposition based learning gobl and cauchy mutation to overcome this problem gobl can provide a faster convergence and the cauchy mutation with a long tail helps trapped particles escape from local optima the proposed approach uses a similar scheme as opposition based differential evolution ode with opposition based population initialization and generation jumping using gobl experiments are conducted on a comprehensive set of benchmark functions including rotated multimodal problems and shifted large scale problems the results show that gopso obtains promising performance on a majority of the test problems

all dimension neighborhood based particle swarm optimization with randomly selected neighbors particle swarm optimization pso is widely used for solving various optimization problems since it has few parameters and is easy to implement however canonical pso generally suffers from premature convergence because it usually loses diversity too rapidly during the evolutionary process to improve the performance of pso on complex problems an all dimension neighborhood based pso with randomly selected neighbors learning strategy adn rsn pso is proposed in this study the randomly selected neighbors rsn learning strategy is adopted in the early stage of pso to enhance the swarm diversity while the all dimension neighborhood adn strategy is utilized in the later stage to accelerate the convergence rate the adn strategy enhances the local search capability around the global best solution in a dimension by dimension manner and the search distance is adapted by shrinking and random expansion operators experimental results show that adn pso can improve the exploitation capability of the global version of pso to test the performance of the proposed adn rsn pso comparison tests on the cec2013 test suite are carried out the comparison results reveal that adn rsn pso outperforms other peer pso variants in the end the proposed adn rsn pso is applied to the radar system design problem to demonstrate its potential in real life applications

an intelligent augmentation of particle swarm optimization with multiple adaptive methods over the last two decades the newly developed optimization technique particle swarm optimization pso has attracted great attention two common criticisms exist first most existing psos are designed for a specific search space thus an algorithm performing well on a diverse set of problems is lacking secondly pso suffers premature convergence to address the first issue we propose to augment pso via the fusion of multiple search methods an intelligent selection mechanism is developed based on an effectiveness index to trigger appropriate search methods in this research two search techniques are studied a non uniform mutation based method and an adaptive sub gradient method we further improve the proposed pso using adaptive cauchy mutation to prevent premature convergence as a result an augmented pso with multiple adaptive methods pso mam is proposed the performance of pso mam is tested on 43 functions uni modal multi modal non separable shifted rotated noisy and mis scaled functions the results are compared in terms of solution quality and convergence speed with 10 published pso methods the experimental results demonstrate pso mam outperforms the comparison algorithms on 36 out of 43 functions we conclude while promising there is still room for improving pso mam on complex multi modal functions e g rotated multi modal functions

the degrees of freedom region and interference alignment for the mimo interference channel with delayed csit the degrees of freedom dof region of the two user multiple input multiple output mimo interference channel ic is studied under the assumptions of fast fading and delayed channel state information csi at the transmitters csit under our fast fading assumption the channel matrices vary independently across time so that delayed csit is equivalent to outdated csit in particular the dof region under delayed csit is established for the general mimo ic with an arbitrary numbers of antennas at each of the four terminals toward this end a set of outer bounds to the dof region of the general mimo ic is derived these bounds are then shown to be tight by developing dof region optimal interference alignment schemes a comparison of the dof region of the mimo ic under the delayed csit assumption with those under the two extremes of instantaneous csit and no csit assumptions is made this comparison reveals that there are nonempty classes of mimo ics defined by certain relationships between the numbers of antennas at the four terminals that correspond to each of the following four scenarios the no csit dof region is strictly contained by or is equal to the delayed csit dof region which in turn is strictly contained by or is equal to the instantaneous csit dof region it is notable that within the class of mimo ics for which the delayed csit dof region is strictly larger than the no csit dof region there is a subclass for which the interference alignment scheme which uses just delayed csit achieves the entire dof region previously known to be achievable only with instantaneous csit

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

across neighborhood search for numerical optimization population based search algorithms pbsas including swarm intelligence algorithms sias and evolutionary algorithms eas are competitive alternatives for solving complex optimization problems and they have been widely applied to real world optimization problems in different fields in this study a novel population based across neighborhood search ans is proposed for numerical optimization ans is motivated by two straightforward assumptions and three important issues raised in improving and designing efficient pbsas in ans a group of individuals collaboratively search the solution space for an optimal solution of the optimization problem considered a collection of superior solutions found by individuals so far is maintained and updated dynamically at each generation an individual directly searches across the neighborhoods of multiple superior solutions with the guidance of a gaussian distribution this search manner is referred to as across neighborhood search the characteristics of ans are discussed and the concept comparisons with other pbsas are given the principle behind ans is simple moreover ans is easy for implementation and application with three parameters being required to tune extensive experiments on 18 benchmark optimization functions of different types show that ans has well balanced exploration and exploitation capabilities and performs competitively compared with many efficient pbsas related matlab codes used in the experiments are available from http guohuawunudt gotoip2 com publications html

a generalized fellegi sunter framework for multiple record linkage with application to homicide record systems we present a probabilistic method for linking multiple datafiles this task is not trivial in the absence of unique identifiers for the individuals recorded this is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation and in general when multiple record systems need to be integrated for posterior analysis our method generalizes the fellegi sunter theory for linking records from two datafiles and its modern implementations the goal of multiple record linkage is to classify the record k tuples coming from k datafiles according to the different matching patterns our method incorporates the transitivity of agreement in the computation of the data used to model matching probabilities we use a mixture model to fit matching probabilities via maximum likelihood using the expectation maximization algorithm we present a method to decide the record k tuples membership to the subsets of matching patterns and we prove its optimality we apply our method to th

unbalanced repeated measures models with structured covariance matrices the question of how to analyze unbalanced or incomplete repeated measures data is a common problem facing analysts we address this problem through maximum likelihood analysis using a general linear model for expected responses and arbitrary structural models for the within subject covariances models that can be fit include standard univariate and multivariate models with incomplete data random effects models and models with time series and factor analytic error structures we describe newton raphson and fisher scoring algorithms for computing maximum likelihood estimates and generalized em algorithms for computing restricted and unrestricted maximum likelihood estimates an example fitting several models to a set of growth data is included

evolutionary programming based on non uniform mutation a new evolutionary programming using non uniform mutation instead of gaussian cauchy and levy mutations is proposed evolutionary programming with non uniform mutation nep has the merits of searching the space uniformly at the early stage and very locally at the later stage during the programming for a suite of 14 benchmark problems nep outperforms the improved evolutionary programming using mutation based on levy probability distribution ilep for multimodal functions with many local minima while being comparable to ilep in performance for unimodal and multimodal functions with only a few minima the detailed theoretical analysis of the executing process of nep and the expected step size on non uniform mutation are given

on the statistical analysis of dirty pictures may 7th 1986 professor a f m smith in the chair summary a continuous two dimensional region is partitioned into a fine rectangular array of sites or pixels each pixel having a particular colour belonging to a prescribed finite set the true colouring of the region is unknown but associated with each pixel there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model the aim is to reconstruct the true scene with the additional knowledge that pixels close together tend to have the same or similar colours in this paper it is assumed that the local characteristics of the true scene can be represented by a nondegenerate markov random field such information can be combined with the records by bayes theorem and the true scene can be estimated according to standard criteria however the computational burden is enormous and the reconstruction may reflect undesirable largescale properties of the random field thus a simple iterative method of reconstruction is proposed which does not depend on these large scale characteristics the method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field some complications including parameter estimation are discussed potential applications are mentioned briefly

regression analysis with linked data record linkage or exact matching can be used to join together two files that contain information on the same individuals but lack unique personal identification codes the possibility of errors in linkage causes problems for estimating the relationships between variables on the two files the effect is analogous to the impact of measurement error a model of a linear regression relationship between variables in linked files is proposed assuming the probabilities that pairs of records are links are known an unbiased estimator of the regression coefficients is derived methods for estimating the linkage probabilities by using mixture models are discussed a consistent estimator of the covariance matrix of the proposed estimator is proposed a bootstrap estimator is used to reflect the impact of the uncertainty in record linkage model parameters on the estimators of the regression parameters a simulation study compares the performance of the proposed estimator and alternatives

bayesian alignment using hierarchical models with applications in protein bioinformatics summary an important problem in shape analysis is to match configurations of points in space after filtering out some geometrical transformation in this paper we introduce hierarchical models for such tasks in which the points in the configurations are either unlabelled or have at most a partial labelling constraining the matching and in which some points may only appear in one of the configurations we derive procedures for simultaneous inference about the matching and the transformation using a bayesian approach our hierarchical model is based on a poisson process for hidden true point locations this leads to consider able mathematical simplification and efficiency of implementation of em and markov chain monte carlo algorithms we find a novel use for classical distributions from directional statistics in a conditionally conjugate specification for the case where the geometrical transformation includes an unknown rotation throughout we focus on the case of affine or rigid motion transformations under a broad parametric family of loss functions an optimal bayesian point estimate of the matching matrix can be constructed that depends only on a single parameter of the family our methods are illustrated by two applications from bioinformatics the first problem is of matching protein gels in two dimensions and the second consists of aligning active sites of proteins in three dimensions in the latter case we also use information related to the grouping of the amino acids as an example of a more general capability of our methodology to include partial labelling information we discuss some open problems and suggest directions for future work

on solving the double loading problem using a modified particle swarm optimization we consider in this paper a double loading problem dlp an np hard optimization problem of extreme economic relevance in industrial areas the problem consists in loading items into bins then stowing bins in a set of compartments the main objective is to minimize the number of used bins we state the mathematical model as well as a modified binary particle swarm optimization with ffd initialization that outperforms state of the art approaches carried out on a large testbed instances

using em to obtain asymptotic variance covariance matrices the sem algorithm abstract the expectation maximization em algorithm is a popular and often remarkably simple method for maximum likelihood estimation in incomplete data problems one criticism of em in practice is that asymptotic variance covariance matrices for parameters e g standard errors are not automatic byproducts as they are when using some other methods such as newton raphson in this article we define and illustrate a procedure that obtains numerically stable asymptotic variance covariance matrices using only the code for computing the complete data variance covariance matrix the code for em itself and code for standard matrix operations the basic idea is to use the fact that the rate of convergence of em is governed by the fractions of missing information to find the increased variability due to missing information to add to the complete data variance covariance matrix we call this supplemented em algorithm the sem algorithm theory and particular examples reinforce the conclusion that the sem alg

on achieving local view capacity via maximal independent graph scheduling if we know more we can achieve more this adage also applies to communication networks where more information about the network state translates into higher sum rates in this paper we formalize this increase of sum rate with increased knowledge of the network state the knowledge of network state is measured in terms of the number of hops h of information available to each transmitter and is labeled as h local view to understand how much capacity is lost due to limited information we propose to use the metric of normalized sum capacity which is the h local view sum capacity divided by global view sum capacity for the cases of one and two local view we characterize the normalized sum capacity for many classes of deterministic and gaussian interference networks in many cases a scheduling scheme called maximal independent graph scheduling is shown to achieve normalized sum capacity we also show that its generalization for 1 local view labeled coded set scheduling achieves normalized sum capacity in some cases where its uncoded counterpart fails to do so

on the convergence properties of the em algorithm two convergence aspects of the em algorithm are studied i does the em algorithm find a local maximum or a stationary value of the incomplete data likelihood function ii does the sequence of parameter estimates generated by em converge several convergence results are obtained under conditions that are applicable to many practical situations two useful special cases are a if the unobserved complete data specification can be described by a curved exponential family with compact parameter space all the limit points of any em sequence are stationary points of the likelihood function b if the likelihood function is unimodal and a certain differentiability condition is satisfied then any em sequence converges to the unique maximum likelihood estimate a list of key properties of the algorithm is included

topological interference management with reconfigurable antennas we study the symmetric degrees of freedom dof of partially connected interference networks under linear coding strategies without channel state information at the transmitters beyond topology we assume that the receivers are equipped with reconfigurable antennas that can switch among their preset modes in such a network setting we characterize the class of network topologies in which half linear symmetric dof is achievable moreover we derive two general upper bounds on the linear symmetric dof for arbitrary network topologies we also demonstrate the tightness of our bounds for the class of network topologies in which all the transmitters in the network have at most two co interferers

a method for calibrating false match rates in record linkage abstract specifying a record linkage procedure requires both 1 a method for measuring closeness of agreement between records typically a scalar weight and 2 a rule for deciding when to classify records as matches or nonmatches based on the weights here we outline a general strategy for the second problem that is for accurately estimating false match rates for each possible cutoff weight the strategy uses a model where the distribution of observed weights are viewed as a mixture of weights for true matches and weights for false matches an em algorithm for fitting mixtures of transformed normal distributions is used to find posterior modes associated posterior variability is due to uncertainty about specific normalizing transformations as well as uncertainty in the parameters of the mixture model the latter being calculated using the sem algorithm this mixture model calibration method is shown to perform well in an applied setting with census data further a simulation experiment reveals that

bayesian estimation of bipartite matchings for record linkage abstractthe bipartite record linkage task consists of merging two disparate datafiles containing information on two overlapping sets of entities this is nontrivial in the absence of unique identifiers and it is important for a wide variety of applications given that it needs to be solved whenever we have to combine information from different sources most statistical techniques currently used for record linkage are derived from a seminal article by fellegi and sunter in 1969 these techniques usually assume independence in the matching statuses of record pairs to derive estimation procedures and optimal point estimators we argue that this independence assumption is unreasonable and instead target a bipartite matching between the two datafiles as our parameter of interest bayesian implementations allow us to quantify uncertainty on the matching decisions and derive a variety of point estimators using different loss functions we propose partial bayes estimates that allow uncertain parts of the bipartite

globally convergent algorithms for maximizing a likelihood function summary in this paper we show global convergence under very general assumptions of iterative maximization procedures with cyclic fixing of groups of parameters maximizing over the remaining parameters further we show that a slightly modified newton procedure applied to the derivative of the reciprocal likelihood function in a one dimensional exponential family is globally convergent we also prove that if the distribution of the sufficient statistic is infinitely divisible then the newton method applied to the derivative of the log likelihood function converges by combining these results we obtain general globally convergent iterative procedures for maximizing the likelihood function in any regular k dimensional exponential family the likelihood function can be maximized either by successively maximizing over one parameter at a time or by maximizing in the newton direction

multivariate stochastic approximation using a simultaneous perturbation gradient approximation the problem of finding a root of the multivariate gradient equation that arises in function minimization is considered when only noisy measurements of the function are available a stochastic approximation sa algorithm for the general kiefer wolfowitz type is appropriate for estimating the root the paper presents an sa algorithm that is based on a simultaneous perturbation gradient approximation instead of the standard finite difference approximation of keifer wolfowitz type procedures theory and numerical experience indicate that the algorithm can be significantly more efficient than the standard algorithms in large dimensional problems

topological interference management with transmitter cooperation interference networks with no channel state information at the transmitter except for the knowledge of the connectivity graph have been recently studied under the topological interference management framework in this paper we consider a similar problem with topological knowledge but in a distributed broadcast channel setting i e a network where transmitter cooperation is enabled we show that the topological information can also be exploited in this case to strictly improve the degrees of freedom dof as long as the network is not fully connected which is a reasonable assumption in practice achievability schemes from graph theoretic and interference alignment perspectives are proposed together with outer bounds built upon generator sequence the concept of compound channel settings and the relation to index coding we characterize the symmetric dof for the so called regular networks with constant number of interfering links and identify the sufficient and or necessary conditions for the arbitrary network topologies to achieve a certain amount of symmetric dof

linear degrees of freedom of the x channel with delayed csit we establish the degrees of freedom dof of the two user x channel with delayed channel knowledge at transmitters i e delayed channel state information at the transmitters csit assuming linear coding strategies at the transmitters we derive a new upper bound and characterize the linear dof of this network to be 6 5 the converse builds upon our development of a general lemma that shows that if two distributed transmitters employ linear strategies the ratio of the dimensions of received linear subspaces at the two receivers cannot exceed 3 2 due to delayed csit as a byproduct we also apply this general lemma to the three user interference channel with delayed csit thereby deriving a new upper bound of 9 7 on its linear dof this is the first bound that captures the impact of delayed csit on the dof of this network under the assumption of linear encoding strategies

interference networks with no csit impact of topology we consider partially connected k user interference networks where the transmitters have no knowledge about the channel gain values but they are aware of network topology we introduce several linear algebraic and graph theoretic concepts to derive new topology based outer bounds and inner bounds on the symmetric degrees of freedom of these networks we evaluate our bounds for two classes of networks to demonstrate their tightness for most networks in these classes quantify the gain of our inner bounds over benchmark interference management strategies and illustrate the effect of network topology on these gains

biometrika centenary theory and general methodology note reprinted in biometrika one hundred years edited by d m titterington and d r cox oxford university press 11 50 reference stat article 2001 002doi 10 1093 biomet 88 1 13view record in web of science record created on 2006 04 21 modified on 2017 05 12