periodicity in a delayed ratio dependent predator prey system abstract with the help of a continuation theorem based on gaines and mawhin s coincidence degree easily verifiable criteria are established for the global existence of positive periodic solutions of a delayed ratio dependent predator prey system in a periodic environment

modelling dependence within joint tail regions standard approaches for modelling dependence within joint tail regions are based on extreme value methods which assume max stability a particular form of joint tail dependence we develop joint tail models based on a broader class of dependence structure which provides a natural link between max stable models and weaker forms of dependence including independence and negative association this approach overcomes many of the problems that are encountered with standard methods and is the basis for a poisson process representation that generalizes existing bivariate results we apply the new techniques to simulated and environmental data and demonstrate the marked advantage that the new approach offers for joint tail extrapolation

pseudo two dimensional shape normalization methods for handwritten chinese character recognition the nonlinear normalization nln method based on line density equalization is popularly used in handwritten chinese character recognition to overcome the insufficient shape restoration capability of one dimensional nln a pseudo two dimensional nln p2dnln method has been proposed and has yielded higher recognition accuracy the p2dnln method however is very computationally expensive because of the line density blurring of each row column in this paper we propose a new pseudo 2d normalization method using line density projection interpolation ldpi which partitions the line density map into soft strips and generate 2d coordinate mapping function by interpolating the 1d coordinate functions that are obtained by equalizing the line density projections of these strips the ldpi method adds little computational overhead to one dimensional nln yet performs comparably well with p2dnln we also apply this strategy to extending other normalization methods including line density projection fitting centroid boundary alignment moment and bi moment methods the latter three methods are directly based on character image instead of line density map their 2d extensions provide real time computation and high recognition accuracy and are potentially applicable to gray scale images and online trajectories

the box cox metric for nearest neighbour classification improvement abstract the nearest neighbour rule is a distribution free classification method in the literature it has been shown however that the performance of the method improves if complex often locally defined metrics are chosen in this paper we demonstrate that in many real world discrimination problems comparable or even better classification results can be obtained with a simple global metric based on the box cox transformation in three case studies this is demonstrated synthetic data the iris data and real radar data in the latter experiment a large reduction in classification error of more than a factor four is achieved copyright 1997 pattern recognition society

persistence and global stability for n species ratio dependent predator prey system with time delays abstract a n species ratio dependent predator prey food chain model with time delays is investigated it is shown that the system is permanent under some appropriate conditions and sufficient conditions are obtained for the global stability of the positive equilibrium of the system

a new class of models for bivariate joint tails summary a fundamental issue in applied multivariate extreme value analysis is modelling dependence within joint tail regions the primary focus of this work is to extend the classical pseudopolar treatment of multivariate extremes to develop an asymptotically motivated representation of extremal dependence that also encompasses asymptotic independence starting with the usual mild bivariate regular variation assumptions that underpin the coefficient of tail dependence as a measure of extremal dependence our main result is a characterization of the limiting structure of the joint survivor function in terms of an essentially arbitrary non negative measure that must satisfy some mild constraints we then construct parametric models from this new class and study in detail one example that accommodates asymptotic dependence asymptotic independence and asymmetry within a straightforward parsimonious parameterization we provide a fast simulation algorithm for this example and detail likelihood based inference including tests for asymptotic dependence and symmetry which are useful for submodel selection we illustrate this model by application to both simulated and real data in contrast with the classical multivariate extreme value approach which concentrates on the limiting distribution of normalized componentwise maxima our framework focuses directly on the structure of the limiting joint survivor function and provides significant extensions of both the theoretical and the practical tools that are available for joint tail modelling

modified quadratic discriminant functions and the application to chinese character recognition issues in the quadratic discriminant functions qdf are discussed and two types of modified quadratic disriminant functions mqdf1 mqdf2 which are less sensitive to the estimation error of the covariance matrices are proposed the mqdf1 is a function which employs a kind of a pseudo bayesian estimate of the covariance matrix instead of the maximum likelihood estimate ordinarily used in the qdf the mqdf2 is a variation of the mqdf1 to save the required computation time and storage two discriminant functions were applied to chinese character recognition to evaluate their effectiveness and remarkable improvement was observed in their performance

modelling across extremal dependence classes different dependence scenarios can arise in multivariate extremes entailing careful selection of an appropriate class of models in bivariate extremes the variables are either asymptotically dependent or are asymptotically independent most available statistical models suit one or other of these cases but not both resulting in a stage in the inference that is unaccounted for but can substantially impact subsequent extrapolation existing modelling solutions to this problem are either applicable only on sub domains or appeal to multiple limit theories we introduce a unified representation for bivariate extremes that encompasses a wide variety of dependence scenarios and applies when at least one variable is large our representation motivates a parametric model that encompasses both dependence classes we implement a simple version of this model and show that it performs well in a range of settings

permanence and stability of a stage structured predator prey model abstract a predator prey model with a stage structure for the predator which improves the assumption that each individual predator has the same ability to capture prey is proposed it is assumed that immature individuals and mature individuals of the predator are divided by a fixed age and that immature predators do not have the ability to attack prey we obtain conditions that determine the permanence of the populations and the extinction of the populations furthermore we establish necessary and sufficient conditions for the local stability of the positive equilibrium of the model by exploiting the monotonicity of one equation of the model we obtain conditions for the global attractivity of the positive equilibrium which allow for long delay as long as the predator birth rate is large or the death rate of immature predators is small by constructing liapunov functionals we also obtain conditions under which the positive equilibrium is globally stable when the delay is small

inferences on the association parameter in copula models for bivariate survival data we investigate two stage parametric and two stage semi parametric estimation procedures for the association parameter in copula models for bivariate survival data where censoring in either or both components is allowed we derive asymptotic properties of the estimators and compare their performance by simulations both parametric and semi parametric estimators of the association parameter are efficient at independence and the parameter estimates in the margins have high efficiency and are robust to misspecification of dependency structures in addition we propose a consistent variance estimator for the semi parametric estimator of the association parameter we apply the proposed methods to an aids data set for illustration

the continuous and differentiable domains of attraction of the extreme value distributions on caracterise les domaines d attraction des distributions de valeurs extremes univariables en utilisant des fonctions de hasard cumulative inverses

periodic solution of a prey predator model with nonlinear state feedback control assume that when the number of pests reaches the certain threshold pest management strategy will be taken to control pests based on this assumption in this paper we propose a pest management model with nonlinear state feedback control we then analyze the dynamic behavior of the model more precisely we first investigate the singularity of the model by using method of qualitative analysis secondly the existence of periodic solution of the model is studied by using successor functions and poincare bendixson theorem and then it is followed by the study of the stability of periodic solution finally an example with numerical simulations is given to illustrate our conclusions

persistence and periodicity of a delayed ratio dependent predator prey model with stage structure and prey dispersal a delayed periodic ratio dependent predator prey model with prey dispersal and stage structure for predator is investigated it is assumed that immature individuals and mature individuals of the predator species are divided by a fixed age and that immature predators don t have the ability to attack prey and that predator species is confined to one of the patches while the prey species can disperse between two patches we first discuss the uniform persistence and impermanence of the model by using gaines and mawhin s continuation theorem of coincidence degree theory a set of easily verifiable sufficient conditions are derived for the existence of positive periodic solutions of the proposed model numerical simulations are presented to illustrate the feasibility of our main results

models and inference for uncertainty in extremal dependence conventionally modelling of multivariate extremes has been based on the class of multivariate extreme value distributions more recently other classes have been developed allowing for the possibility that whilst dependence is observed at finite levels the limit distribution is independent a number of articles have shown this development to be important for accurate estimation of the extremal properties both of theoretical processes and observed datasets it has also been shown that so far as dependence is concerned the choice between modelling with either asymptotically dependent or asymptotically independent distributions can be far more influential than model choice within either of these two classes in this paper we explore the issue of modelling across both classes examining in particular the effect of uncertainty caused by lack of knowledge about the status of asymptotic dependence this is achieved by new multivariate models whose parameter spaces are such that asymptotic dependence occurs on a boundary standard techniques in bayesian inference implemented through markov chain monte carlo enable inferences to be drawn that assign posterior probability mass to the boundary region the techniques are illustrated on a set of oceanographic data for which previous analyses have shown that it is difficult to resolve the question of asymptotic dependence status which is however important in model extrapolation

adaptive normalization of handwritten characters using global local affine transformation this paper introduces an adaptive or category dependent normalization method that normalizes an input pattern against each reference pattern using global local affine transformation gat lat in a hierarchical manner as a general deformation model also the normalization criterion is clearly defined as minimization of the mean of nearest neighbor interpoint distances between each reference pattern and a normalized input pattern optimal gat lat is determined by iterative application of weighted least squares fitting techniques experiments using input patterns of 3 171 character categories including kanji kana and alphanumerics written by 36 people in the cursive style against square style reference patterns show that the proposed method not only can absorb a fairly large amount of handwriting fluctuation within the same category but the discrimination ability is greatly improved by the suppression of excessive normalization against similarly shaped but different categories furthermore comparative results obtained by the conventional shape normalization method for preprocessing are presented

discriminative quadratic feature learning for handwritten chinese character recognition in this paper we propose a feature learning method for handwritten chinese character recognition hccr called discriminative quadratic feature learning dqfl based on original gradient direction feature representation quadratic correlation between features is used to promote the feature dimensionality then discriminative feature extraction dfe is used for dimensionality reduction by combining dimensionality promotion and reduction we can learn a much more discriminative and nonlinear feature representation which can then boost the classification accuracy significantly for dimensionality promotion two types of correlation are exploited namely statistical correlation and spatial correlation statistical correlation is computed on multiple local feature vectors in different regions of the character image while spatial correlation encodes the dependency between features of two positions feature correlation increases the dimensionality by over 40 000 dfe then reduces the dimensionality to less than 300 without losing discriminability classification is performed using nearest prototype classifier npc modified quadratic discriminant function mqdf and discriminative learning quadratic discriminant function dlqdf in experiments on the casia hwdb1 1 standard dataset the proposed dqfl method improves the test accuracies of npc mqdf and dlqdf by 4 94 1 83 and 1 82 respectively the test accuracy is further improved by training set expansion on the icdar 2013 chinese handwriting recognition competition dataset the proposed dqfl dlqdf classifier outperforms the best participating system based on deep convolutional neural network cnn while the test speed is much faster highlightswe propose a feature learning method for handwritten chinese character recognition quadratic correlation between original gradient features is utilized discriminative learning guarantees the discriminability of quadratic features the proposed method outperforms deep convolutional neural networks with much faster test speed

modeling spatial processes with unknown extremal dependence class abstractmany environmental processes exhibit weakening spatial dependence as events become more extreme well known limiting models such as max stable or generalized pareto processes cannot capture this which can lead to a preference for models that exhibit a property known as asymptotic independence however weakening dependence does not automatically imply asymptotic independence and whether the process is truly asymptotically in dependent is usually far from clear the distinction is key as it can have a large impact upon extrapolation that is the estimated probabilities of events more extreme than those observed in this work we present a single spatial model that is able to capture both dependence classes in a parsimonious manner and with a smooth transition between the two cases the model covers a wide range of possibilities from asymptotic independence through to complete dependence and permits weakening dependence of extremes even under asymptotic dependence censored likelihood based

multivariate archimedean copulas d monotone functions and l1 norm symmetric distributions it is shown that a necessary and sufficient condition for an archimedean copula generator to generate a d dimensional copula is that the generator is a d monotone function the class of d dimensional archimedean copulas is shown to coincide with the class of survival copulas of d dimensional l 1 norm symmetric distributions that place no point mass at the origin the d monotone archimedean copula generators may be characterized using a little known integral transform of williamson duke math j 23 1956 189 207 in an analogous manner to the well known bernstein widder characterization of completely monotone generators in terms of the laplace transform these insights allow the construction of new archimedean copula families and provide a general solution to the problem of sampling multivariate archimedean copulas they also yield useful expressions for the d dimensional kendall function and kendall s rank correlation coefficients and facilitate the derivation of results on the existence of densities and the description of singular components for archimedean copulas the existence of a sharp lower bound for archimedean copulas with respect to the positive lower orthant dependence ordering is shown

permanence and periodicity of a delayed ratio dependent predator prey model with stage structure a periodic ratio dependent predator prey model with time delays and stage structure for both prey and predator is investigated it is assumed that immature individuals and mature individuals of each species are divided by a fixed age and that immature predators do not have the ability to attack prey sufficient conditions are derived for the permanence and existence of positive periodic solutions of the model numerical simulations are presented to illustrate the feasibility of our main results

stability of stage structured population models in order to model populations which go through distinct stages such as laboratory insect populations it is necessary to take the age structure of the population into account the oscillations which have frequently been observed in the sizes of such populations cannot be explained by a model which ignores the effects of age structure while it is feasible to use a fully age structured model of mckendrickkvon foerster type 3 or an analogous size structured model 4 it is generally impossible to derive qualitative information about the behavior of solutions of such models accordingly models have been proposed which subdivide the lifetime of an individual into two stages larvae and adults with different dynamics in each stage such models may be formulated as systems of differentialdifference equations and several different types of systems of differentialdifference equations to model different types of larval competition have been proposed in s our purpose is to analyze the stability properties of the various models proposed in s using the standard method of determining equilibria linearizing about an equilibrium and locating the roots of the corresponding characteristic equation by an equilibrium of a system of differential difference equations we mean simply a constant solution thus equilibria are found by setting the right side of each equation equal to zero assuming that each equation of the system has been solved for the derivatives of the unknown functions in cases where every constant is a solution of one of the equations in a system it is necessary to replace that equation by an integrated form which incorporates the initial data in order to solve for equilibria the linearization of a system about an equilibrium is obtained by using the deviations of the unknown functions from equilibrium as new variables expanding by taylor s theorem and retaining only the linear terms the

analysis of a model representing stage structured population growth with state dependent time delay a stage structured model of population growth is proposed where the time to ma turity is itself state dependent it is shown that under appropriate assumptions all solutions are positive and bounded criteria for the existence of positive equilibria and further conditions for the uniqueness of the equilibria are given the stability of the equilibria are also discussed in addition an attracting region is determined for solutions such that this region collapses to the unique positive equilibrium in the state independent case

periodic solutions and permanence for a delayed nonautonomous ratio dependent predator prey model with holling type functional response by using the continuation theorem of coincidence degree theory the existence of positive periodic solutions for a delayed ratio dependent predator prey model with holling type iii functional response x t x t a t b t t k t s x s ds c t x2 t y t m2y2 t x2 t y t y t e t x2 t m2y2 t x2 t d t is established where a t b t c t e t and d t are all positive periodic continuous functions with period 0 m 0 and k s is a measurable function with period is a nonnegative constant the permanence of the system is also considered in particular if k s 0 s where 0 s is the dirac delta function at s 0 our results show that the permanence of the above system is equivalent to the existence of positive periodic solution

improvement of handwritten japanese character recognition using weighted direction code histogram several algorithms for preprocessing feature extraction pre classification and main classification are experimentally compared to improve the recognition accuracy of handwritten japanese character recognition the compared algorithms are three types of nonlinear normalization for the preprocessing the discriminant analysis and the principal component analysis for the feature extraction the minimum distance classifiers and the linear classifier for the high speed pre classification and modified bayes classifier and subspace method for the robust main classification the performance of the recognition algorithm is fully tested using the etl9b character database the recognition accuracy of 99 15 at the recognition speed of eight characters per second is achieved this accuracy is the best one ever reported for the database

a conditional approach for multivariate extreme values with discussion summary multivariate extreme value theory and methods concern the characterization estimation and extrapolation of the joint tail of the distribution of a d dimensional random variable existing approaches are based on limiting arguments in which all components of the variable become large at the same rate this limit approach is inappropriate when the extreme values of all the variables are unlikely to occur together or when interest is in regions of the support of the joint distribution where only a subset of components is extreme in practice this restricts existing methods to applications where d is typically 2 or 3 under an assumption about the asymptotic form of the joint distribution of a d dimensional random variable conditional on its having an extreme component we develop an entirely new semiparametric approach which overcomes these existing restrictions and can be applied to problems of any dimension we demonstrate the performance of our approach and its advantages over existing methods by using theoretical examples and simulation studies the approach is used to analyse air pollution data and reveals complex extremal dependence behaviour that is consistent with scientific understanding of the process we find that the dependence structure exhibits marked seasonality with ex tremal dependence between some pollutants being significantly greater than the dependence at non extreme levels

a nonlinear normalization method for handprinted kanji character recognition line density equalization abstract a nonlinear normalization method the line density equalization is proposed where a resampling is done so as to equate the product of a local line density and a variable sampling pitch consequently the line density in the space is homogenized the efficiency of utilization of the space is increased and a stable normalization is obtained for partially irregular shape variations the method was applied to handprinted kanji character recognition and its effectiveness has been demonstrated

a predator prey system with stage structure for predator abstract this paper studies the asymptotoc behavior of a predator prey model with stage structure it is found that an orbitally asymptotically stable periodic orbit exists in that model when time delay due to gestation of predator and time delay from crowding effect of prey are incorporated we establish the condition for the permanence of populations and sufficient conditions under which positive equilibrium of the model is globally stable

extinction and permanence in nonautonomous competitive system with stage structure in this paper the n species nonautonomous stage structured competitive system is constructed and considered sufficient conditions for its extinction and permanence are obtained results here generalize and unify some previous ones moreover it is concluded that stage structure in this system is one of the important factors that effect the extinction and permanence of species

permanence and periodicity of a delayed ratio dependent predator prey model with holling type functional response and stage structure a periodic and delayed ratio dependent predator prey system with holling type iii functional response and stage structure for both prey and predator is investigated it is assumed that immature predator and mature individuals of each species are divided by a fixed age and immature predator do not have the ability to attack prey sufficient conditions are derived for the permanence and existence of positive periodic solution of the model numerical simulations are presented to illustrate the feasibility of our main results