estimation and statistical analysis of exponential polynomial signals we derive and analyze a suboptimal algorithm for estimating the unknown parameters of an exponential polynomial signal eps model the eps model represents a signal as an exponent raised to a complex polynomial of time that is the phase and the logarithm of the amplitude are approximated as finite order polynomials of time

random graph models for directed acyclic networks we study random graph models for directed acyclic graphs a class of networks that includes citation networks food webs and feed forward neural networks among others we propose two specific models roughly analogous to the fixed edge number and fixed edge probability variants of traditional undirected random graphs we calculate a number of properties of these models including particularly the probability of connection between a given pair of vertices and compare the results with real world acyclic network data finding that theory and measurements agree surprisingly well far better than the often poor agreement of other random graph models with their corresponding real world networks

image watermarking based on invariant regions of scale space representation this paper proposes a novel content based image watermarking method based on invariant regions of an image the invariant regions are self adaptive image patches that deform with geometric transformations three different invariant region detection methods based on the scale space representation of an image were considered for watermarking at each invariant region the watermark is embedded after geometric normalization according to the shape of the region by binding watermarking with invariant regions resilience against geometric transformations can be readily obtained experimental results show that the proposed method is robust against various image processing steps including geometric transformations cropping filtering and jpeg compression

model selection and akaike s information criterion aic the general theory and its analytical extensions during the last fifteen years akaike s entropy based information criterion aic has had a fundamental impact in statistical model evaluation problems this paper studies the general theory of the aic procedure and provides its analytical extensions in two ways without violating akaike s main principles these extensions make aic asymptotically consistent and penalize overparameterization more stringently to pick only the simplest of the true models these selection criteria are called caic and caicf asymptotic properties of aic and its extensions are investigated and empirical performances of these criteria are studied in choosing the correct degree of a polynomial model in two different monte carlo experiments under different conditions

recognition using region correspondences recognition systems attempt to recover information about the identity of observed objects and their location in the environment a fundamental problem in recognition is pose estimation this is the problem of using a correspondence between some portions of an object model and some portions of an image to determine whether the image contains an instance of the object and in case it does to determine the transformation that relates the model to the image the current approaches to this problem are divided into methods that use global properties of the object e g centroid and moments of inertia and methods that use local properties of the object e g corners and line segments global properties are sensitive to occlusion and specifically to self occlusion local properties are difficult to locate reliably and their matching involves intensive computation we present a novel method for recognition that uses region information in our approach the model and the image are divided into regions given a match between subsets of regions without any explicit correspondence between different pieces of the regions the alignment transformation is computed the method applies to planar objects under similarity affine and projective transformations and to projections of 3 d objects undergoing affine and projective transformations the new approach combines many of the advantages of the previous two approaches while avoiding some of their pitfalls like the global methods our approach makes use of region information that reflects the true shape of the object but like local methods our approach can handle occlusion

global description of edge patterns using moments abstract shape recognition has traditionally been accomplished using well bounded segmented regions or their closed contours in this way the internal edge details of the objects are ignored there are cases such as in character recognition and in object recognition when patterns are not closed contours in this paper such patterns are defined as edge patterns and edge standard moments esm are developed which are invariant to location scale and rotation results of patterns matching with character recognition and polyhedral object recognition are presented

computer vision for robust 3d aircraft recognition with fast library search abstract a fast but accurate classification method is presented which takes the fundamental problems of aircraft recognition in 3d space into consideration elliptic fourier descriptors are used for aircraft classification and pose determination from a two dimensional image recorded at an arbitrary viewing angle the computation model of the nearest neighbor classification rule or simply nnr is analysed to come up with proper necessary conditions used by our method for reducing the set of near neighbors of a test projection for a fast partial library search on the other hand our method is robust to noise by incorporating a perturbation analysis of feature variation into the classifier design both graphics generated data and real data of five aircraft have been used to demonstrate the applicability of the new method the results indicate that the method is almost as accurate as the nnr method but is much faster the method is also shown to outperform other existing classification methods including the decision tree classification method

model based gaussian and non gaussian clustering abstract the classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms including those based on the sum of squares criterion and on the criterion of friedman and rubin 1967 however as currently implemented it does not allow the specification of which features orientation size and shape are to be common to all clusters and which may differ between clusters also it is restricted to gaussian distributions and it does not allow for noise we propose ways of overcoming these limitations a reparameterization of the covariance matrix allows us to specify that some features but not all be the same for all clusters a practical framework for non gaussian clustering is outlined and a means of incorporating noise in the form of a poisson process is described an approximate bayesian method for choosing the number of clusters is given the performance of the proposed methods is studied by simulation with encouraging results the methods are applied to the analysis of a data set arising in the study of diabetes and the results seem better than those of previous analyses rh

the method of normalization to determine invariants the determination of invariant characteristics is an important problem in pattern recognition many invariants are known which have been obtained either by normalization or by other methods this paper shows that the method of normalization is much more general and allows one to derive a lot of sets of invariants from the second list as well to this end the normalization method is generalized and is presented in such a way that it is easy to apply thus unifying and simplifying the determination of invariants furthermore this paper discusses the advantages and disadvantages of the invariants obtained by normalization their main advantage is that the normalization process provides us with a standard position of the object because of the generality of the method also new invariants are obtained such as normalized moments more stable than known ones legendre descriptors and zernike descriptors to affine transformations two dimensional fourier descriptors and affine moment invariants obtained by combining hu s moment invariants and normalized moments

supervised bayesian latent class models for high dimensional data high grade gliomas are the most common primary brain tumors in adults and are typically diagnosed using histopathology however these diagnostic categories are highly heterogeneous and do not always correlate well with survival in an attempt to refine these diagnoses we make several immunohistochemical measurements of ykl 40 a gene previously shown to be differentially expressed between diagnostic groups we propose two latent class models for classification and variable selection in the presence of high dimensional binary data fit by using bayesian markov chain monte carlo techniques penalization and model selection are incorporated in this setting via prior distributions on the unknown parameters the methods provide valid parameter estimates under conditions in which standard supervised latent class models do not and outperform two stage approaches to variable selection and parameter estimation in a variety of settings we study the properties of these methods in simulations and apply these methodologies to the glioma study for which identifiable three class parameter estimates cannot be obtained without penalization with penalization the resulting latent classes correlate well with clinical tumor grade and offer additional information on survival prognosis that is not captured by clinical diagnosis alone the inclusion of ykl 40 features also increases the precision of survival estimates fitting models with and without ykl 40 highlights a subgroup of patients who have glioblastoma gbm diagnosis but appear to have better prognosis than the typical gbm patient

fuzzy artmap classification of invariant features derived using angle of rotation from a neural network conventional regular moment functions have been proposed as pattern sensitive features in image classification and recognition applications but conventional regular moments are only invariant to translation rotation and equal scaling it is shown that the conventional regular moment invariants remain no longer invariant when the image is scaled unequally in the x and y axis directions we address this problem by presenting a technique to make the regular moment functions invariant to unequal scaling however the technique produces a set of features that are only invariant to translation unequal equal scaling and reflection they are not invariant to rotation to make them invariant to rotation moments are calculated with respect to the principal axis of the image to perform this the exact angle of rotation must be known but the method of using the second order moments to determine this angle will also be inclusive of an undesired tilt angle therefore in order to correctly determine the amount of rotation the tilt angle which differs for different scaling factors in the x and y axis directions for the particular image must be obtained in order to solve this problem a neural network using the back propagation learning algorithm is trained to estimate the tilt angle of the image and from this the amount of rotation for the image can be determined next the new moments are derived and a fuzzy artmap network is used to classify these images into their respective classes sets of experiments involving images rotated and scaled unequally in the x and y axis directions are carried out to demonstrate the validity of the proposed technique

escaping from cycles through a glass transition a random walk is performed over a disordered media composed of n sites random and uniformly distributed inside a d dimensional hypercube the walker cannot remain in the same site and hops to one of its n neighboring sites with a transition probability that depends on the distance d between sites according to a cost function e d the stochasticity level is parametrized by a formal temperature t in the case t 0 the walk is deterministic and ergodicity is broken the phase space is divided in a o n number of attractor basins of two cycles that trap the walker for d 1 analytic results indicate the existence of a glass transition at t 1 1 2 as n infinity below t1 the average trapping time in two cycles diverges and an out of equilibrium behavior appears similar glass transitions occur in higher dimensions when the right cost function is chosen we also present some results for the statistics of distances for poisson spatial point processes

clustering with block mixture models abstract basing cluster analysis on mixture models has become a classical and powerful approach until now this approach which allows to explain some classic clustering criteria such as the well known k means criteria and to propose general criteria has been developed to classify a set of objects measured on a set of variables but for this kind of data if most clustering procedures are designated to construct an optimal partition of objects or sometimes of variables there exist others methods named block clustering methods which consider simultaneously the two sets and organize the data into homogeneous blocks in this work a new mixture model called block mixture model is proposed to take into account this situation this model allows to embed simultaneous clustering of objects and variables in a mixture approach we first consider this probabilistic model in a general context and we develop a new algorithm of simultaneous partitioning based on the cem algorithm then we focus on the case of binary data and we show that our approach allows us to extend a block clustering method which had been proposed in this case simplicity fast convergence and the possibility to process large data sets are the major advantages of the proposed approach

asymptotically efficient selection of the order by the criterion autoregressive transfer function on montre que la propriete d optimalite obtenue par shibata 1980 1981 est valable pour l approche cat parzen 1974 cat parzen 1977 et cat 2 bhansali 1985

spectral theory of sparse non hermitian random matrices sparse non hermitian random matrices arise in the study of disordereda 13 physical systems with asymmetric local interactions and have applications ranginga 13 from neural networks to ecosystem dynamics the spectral characteristics of thesea 13 matrices provide crucial information on system stability and susceptibility however a 13 their study is greatly complicated by the twin challenges of a lack of symmetry anda 13 a sparse interaction structure in this review we provide a concise and systematica 13 introduction to the main tools and results in this field we show how the spectraa 13 of sparse non hermitian matrices can be computed via an analogy with infinitea 13 dimensional operators obeying certain recursion relations with reference to threea 13 illustrative examples adjacency matrices of regular oriented graphs adjacencya 13 matrices of oriented erd s renyi graphs and adjacency matrices of weighted orienteda 13 erd s renyi graphs we demonstrate the use of these methods to obtain both analytica 13 and numerical results for the spectrum the spectral distribution the location of outliera 13 eigenvalues and the statistical properties of eigenvectors a 13

a general approach to moment calculation for polygons and line segments abstract the calculation of moments from the boundary of a solid digital area is a quite useful technique used in pattern recognition a straightforward way of doing the calculation that does not have any numerical instabilities is presented all the symmetries available in moment calculations are preserved and it is shown how different moment formulas are related to each other this includes relations between different moments and the relationships among different methods for calculating moments finally a direct relation between the moment expansion of a polygonal area and a stick figure made from one dimensional lines embedded in a two dimensional plane is derived

invariant image recognition by zernike moments the problem of rotation scale and translation invariant recognition of images is discussed a set of rotation invariant features are introduced they are the magnitudes of a set of orthogonal complex moments of the image known as zernike moments scale and translation invariance are obtained by first normalizing the image with respect to these parameters using its regular geometrical moments a systematic reconstruction based method for deciding the highest order zernike moments required in a classification problem is developed the quality of the reconstructed image is examined through its comparison to the original one the orthogonality property of the zernike moments which simplifies the process of image reconstruction make the suggest feature selection approach practical features of each order can also be weighted according to their contribution to the reconstruction process the superiority of zernike moment features over regular moments and moment invariants was experimentally verified

insights into biological information processing structural and dynamical analysis of a human protein signalling network we present an investigation on the structural and dynamical properties of a human protein signalling network hpsn this biological network is composed of nodes that correspond to proteins and directed edges that represent signal flows in order to gain insight into the organization of cell information processing this network is analysed taking into account explicitly the edge directions we explore the topological properties of the hpsn at the global and the local scale further applying the generating function formalism to provide a suitable comparative model the relationship between the node degrees and the distribution of signals through the network is characterized using degree correlation profiles finally we analyse the dynamical properties of small sub graphs showing high correlation between their occurrence and dynamic stability

assessing a mixture model for clustering with the integrated completed likelihood we propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood for this purpose the observed data are assigned to unknown clusters using a maximum a posteriori operator then the integrated completed likelihood icl is approximated using the bayesian information criterion bic numerical experiments on simulated and real data of the resulting icl criterion show that it performs well both for choosing a mixture model and a relevant number of clusters in particular icl appears to be more robust than bic to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data

pattern recognition with moment invariants a comparative study and new results abstract moment invariants have been proposed as pattern sensitive features in classification and recognition applications in this paper the authors present a comprehensive study of the effectiveness of different moment invariants in pattern recognition applications by considering two sets of data handwritten numerals and aircrafts the authors also present a detailed study of zernike and pseudo zernike moment invariants including a new procedure for deriving the moment invariants in addition the authors introduce a new normalization scheme that reduces the large dynamic range of these invariants as well as implicit redundancies in these invariants based on a comprehensive study with both handwritten numerals and aircraft data the authors show that the new method of deriving zernike moment invariants along with the new normalization scheme yield the best overall performance even when the data are degraded by additive noise

model averaging by jackknife criterion in models with dependent data abstract the past decade witnessed a literature on model averaging by frequentist methods for the most part the asymptotic optimality of various existing frequentist model averaging estimators has been established under i i d errors recently hansen and racine hansen b e racine j 2012 jackknife model averaging journal of econometrics 167 38 46 developed a jackknife model averaging jma estimator which has an important advantage over its competitors in that it achieves the lowest possible asymptotic squared error under heteroscedastic errors in this paper we broaden hansen and racine s scope of analysis to encompass models with i a non diagonal error covariance structure and ii lagged dependent variables thus allowing for dependent data we show that under these set ups the jma estimator is asymptotically optimal by a criterion equivalent to that used by hansen and racine a monte carlo study demonstrates the finite sample performance of the jma estimator in a variety of model settings

mean field level analysis of epidemics in directed networks the susceptible infected removed spreading model in a directed graph is studied the mean field level rate equations are built with the degree degree connectivity correlation element and the in out degree distribution and the outbreak threshold is obtained analytically it is determined by the combination of connectivity probability and the degree distribution furthermore the methods of calculating the degree degree correlations in directed networks are presented the numerical results of the discrete epidemic processes in networks verify our analyses

signal recognition fourier transform vs hartley transform abstract the new generic feature representation approach was utilized for gaussian recognition approach consists of using simultaneously two new recognition features real and imaginary fourier components with taking into account the covariance between features advanced time frequency technique short time fourier transform was considered the recognition effectiveness between the new approach and hartley based approach was compared it was shown for gaussian recognition that hartley approach is not an optimal and is not even a particular case of the proposed approach the use of the proposed approach provides an essential effectiveness gain in comparison with hartley approach

exploring the model space and ranking a best class of models in surface wave dispersion inversion application at european strong motion sites abstractthe inversion of surface wave dispersion curve to derive shear wave velocity profile is a very delicate process dealing with a nonunique problem which is strongly dependent on the model space parameterization when independent and reliable information is not available the selection of most representative models within the ensemble produced by the inversion is often difficult we implemented a strategy in the inversion of dispersion curves able to investigate the influence of the parameterization of the model space and to select a best class of models we analyzed surface wave dispersion curves measured at 14 european strong motion sites within the neries ec project we focused on the inversion task exploring the model space by means of four distinct parameterization classes composed of layers progressively added over a half space the classes differ in the definition of the shear wave velocity profile we considered models with uniform velocity as well as models with increasing velocity with d

percolation in directed scale free networks many complex networks in nature have directed links a property that affects the network s navigability and large scale topology here we study the percolation properties of such directed scale free networks with correlated in and out degree distributions we derive a phase diagram that indicates the existence of three regimes determined by the values of the degree exponents in the first regime we regain the known directed percolation mean field exponents in contrast the second and third regimes are characterized by anomalous exponents which we calculate analytically in the third regime the network is resilient to random dilution i e the percolation threshold is pc 1

two dimensional rigid body kinematics using image contour registration a method for calculating two dimensional rigid body kinematic parameters using shape features is presented proposed applications include the noninvasive quantification of planar joint motion in vivo by using digitized images computed tomographs radiographs etc of a bone contour at two positions the contour curvatures can be best fit to obtain a one to one mapping or registration of the bone images this produces a dense field of displacement vectors from which planar rigid body kinematic parameters can be estimated accuracy was studied using radiographic images of cadaveric femoral bone the two motions of pure rotation with a fixed center of rotation and of pure translation were simulated for pure rotation error in rotation was independent of the rotation magnitude with an average n 10 error of 0 3 0 8 degrees the translation error averaged 0 9 0 5 mm for pure translation the error in rotation was 0 01 0 69 degrees and the error in translation was 0 62 0 98 mm n 10 this novel method has broad applications in the field of planar kinematics especially in cases for which marker fixation is neither possible nor practical

regression and time series model selection in small samples summary a bias correction to the akaike information criterion aic is derived for regression and autoregressive time series models the correction is of particular use when the sample size is small or when the number of fitted parameters is a moderate to large fraction of the sample size the corrected method called aicc is asymptotically efficient if the true model is infinite dimensional furthermore when the true model is of finite dimension aicc is found to provide better model order choices than any other asymptotically efficient method applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed

effect of node deleting on network structure abstract the ever increasing knowledge of the structure of various real world networks has uncovered their complex multi mechanism governed evolution processes therefore a better understanding of the structure and evolution of these networked complex systems requires us to describe such processes in a more detailed and realistic manner in this paper we introduce a new type of network growth rule which comprises addition and deletion of nodes and propose an evolving network model to investigate the effect of node deleting on network structure it is found that with the introduction of node deleting network structure is significantly transformed in particular degree distribution of the network undergoes a transition from scale free to exponential forms as the intensity of node deleting increases at the same time nontrivial disassortative degree correlation develops spontaneously as a natural result of network evolution in the model we also demonstrate that node deleting introduced in the model does not destroy the connectedness of a growing network so long as the increasing rate of edges is not excessively small in addition it is found that node deleting will weaken but not eliminate the small world effect of a growing network and generally it will decrease the clustering coefficient in a network

reachability and recoverability of sink nodes in growing acyclic directed networks abstract we study the growth of networks from a set of isolated ground nodes by the addition of one new node per time step and also of a fixed number of directed edges leading from the new node to randomly selected nodes already in the network a fixed width time window is used so that in general only nodes that entered the network within the latest window may receive new incoming edges the resulting directed network is acyclic at all times and allows some of the ground nodes then called sinks to be reached from some of the non ground nodes we regard such networks as representative of abstract systems of partially ordered constituents for example in some of the domains related to technological evolution two properties of interest are the number of sinks that can be reached from a randomly chosen non ground node its reach and for a fixed sink the number of nonoverlapping directed paths through which the sink can be reached at a given time from some of the latest nodes to have entered the network we demonstrate by means of simulations and also of analytic characterizations that reaches are distributed according to a power law and that the desired directed paths are expected to occur in very small numbers perhaps indicating that recovering sinks late in the process of network growth is strongly sensitive to accidental path disruptions

kauffman boolean model in undirected scale free networks we investigate analytically and numerically the critical line in undirected random boolean networks with arbitrary degree distributions including the scale free topology of connections p k ensuremath sim k ensuremath ensuremath gamma we explain that the unattainability of the critical line in numerical simulations of classical random graphs is due to percolation phenomena we suggest that recent findings of discrepancy between simulations and theory in directed random boolean networks might have the same reason we also show that in infinite scale free networks the transition between frozen and chaotic phases occurs for 3l ensuremath gamma l3 5 since most critical phenomena in scale free networks reveal their nontrivial character for ensuremath gamma l3 the position of the critical line in the kauffman model seems to be an important exception to the rule

properties and estimation of asymmetric exponential power distribution the new distribution class asymmetric exponential power distribution aepd proposed in this paper generalizes the class of skewed exponential power distributions sepd in a way that in addition to skewness introduces different decay rates of density in the left and right tails our parametrization provides an interpretable role for each parameter we derive moments and moment based measures skewness kurtosis expected shortfall it is demonstrated that a maximum entropy property holds for the aepd distributions we establish consistency asymptotic normality and efficiency of the maximum likelihood estimators over a large part of the parameter space by dealing with the problems created by non smooth likelihood function and derive explicit analytical expressions of the asymptotic covariance matrix where the results apply to the sepd class they enlarge on the current literature also we give a convenient stochastic representation of the distribution our monte carlo study illustrates the theoretical results we also provide some empirical evidence for the usefulness of employing aepd errors in garch type models for predicting downside market risk of financial assets

translation rotation and scale invariant pattern recognition by high order neural networks and moment classifiers the classification and recognition of two dimensional patterns independently of their position orientation and size by using high order networks are discussed a method is introduced for reducing and controlling the number of weights of a third order network used for invariant pattern recognition the method leads to economical networks that exhibit high recognition rates for translated rotated and scaled as well as locally distorted patterns the performance of these networks at recognizing types and handwritten numerals independently of their position size and orientation is compared with and found superior to the performance of a layered feedforward network to which image features extracted by the method of moments are presented as input

detecting the solution space of vertex cover by mutual determinations and backbones to solve the combinatorial optimization problems especially the minimal vertex cover problem with high efficiency is a significant task in theoretical computer science and many other subjects aiming at detecting the solution space of vertex cover a new structure named mutual determination is defined and discovered for vertex cover on general graphs which results in the emergence of strong correlations among the unfrozen nodes based on the backbones and mutual determinations with node ranks by leaf removal we propose a mutual determination and backbone evolution algorithm to achieve the reduced solution graph which provides a graphical expression of the solution space of vertex cover by this algorithm the whole solution space and detailed structures such as backbones can be obtained strictly when there is no leaf removal core on the given graph compared with the current algorithms the mutual determination and backbone evolution algorithm performs as well as the replica symmetry one in a certain interval but has a small gap higher than the replica symmetric breaking one and has a relatively small error for the exact results the algorithm with the mutual determination provides a new viewpoint to solve vertex cover and understand the organizations of the solution spaces and the reduced solution graph gives an alternative way to catch detailed information of the ground steady states

three dimensional shape analysis using moments and fourier descriptors a procedure for using moment based feature vectors to identify a three dimensional object from a two dimensional image recorded at an arbitrary viewing angle and range is presented a moment form called standard moments rather than the usual moment invariants is considered a standard six airplane experiment was used to compare different techniques fourier descriptors and moment invariants were both compared to the present scheme for normalized moments various experiments were conducted using mixtures of silhouette and boundary moments and different normalization techniques standard moments gave slightly better results than fourier descriptors for this experiment both of these techniques were much better than moment invariants

fast computation of 2 d image moments using biaxial transform abstract in this paper a new transform is introduced this transform is based on dividing the image into four quadrants the transform can be generated by adding or subtracting the gray levels of each two axially symmetric points in the original image the addition or subtraction is dependent on the quadrant position the newly transformed quadrants can then be used to compute the moments only one quadrant of transformed image is needed to compute the moments of specific orders this can lead to considerable savings in both processing time as well as storage other advantages of this transform such as image reconstruction and using multiple filters on the same image frame to remove noise are also explored

returnability in complex directed networks digraphs the concept of returnability is proposed for complex directed networks digraphs it can be seen as a generalization of the concept of reciprocity two measures of the returnability are introduced we establish closed formulas for the calculation of the returnability measures which are also related to the digraph spectrum the two measures are calculated for simple examples of digraphs as well as for real world complex directed networks and are compared with the reciprocity

identification of three dimensional objects using range information a method for identifying unoccluded three dimensional objects from arbitrary viewing angles is presented the technique uses synthetically generated range data in a model based feature vector classification scheme fourier descriptors and moments are used for feature vector generation from respectively contour imagery and silhouette or range imagery a method is developed for generating an exhaustive set of library views and worst case test views that is based on a polyhedral approximation to a sphere analysis of the success of this approach is made with experiments on a six airplane data set a model of range data noise is developed and results are presented for both ideal and noisy lower resolution image classification tests the use of multiple views for object identification is discussed and results for one two and three view tests are presented

location estimation of a random signal source based on correlated sensor observations the problem of location estimation of a source of random signals using a network of sensors is considered a novel maximum likelihood estimation mle based approach using copula functions is proposed the measurements received at the sensors are often spatially correlated and characterized by a multivariate distribution using the theory of copulas the joint parametric density of sensor observations joint likelihood is approximated assuming only the knowledge of the marginal likelihood functions of the sensor observations the problem of selecting the best copula function to model the joint likelihood is approached as one of model selection and a model fusion strategy is used to reduce the effect of selection bias an example involving source localization of a poisson source is presented to illustrate the proposed approach and demonstrate its performance

an em algorithm for the block mixture model although many clustering procedures aim to construct an optimal partition of objects or sometimes of variables there are other methods called block clustering methods which consider simultaneously the two sets and organize the data into homogeneous blocks recently we have proposed a new mixture model called block mixture model which takes into account this situation this model allows one to embed simultaneous clustering of objects and variables in a mixture approach we have studied this probabilistic model under the classification likelihood approach and developed a new algorithm for simultaneous partitioning based on the classification em algorithm in this paper we consider the block clustering problem under the maximum likelihood approach and the goal of our contribution is to estimate the parameters of this model unfortunately the application of the em algorithm for the block mixture model cannot be made directly difficulties arise due to the dependence structure in the model and approximations are required using a variational approximation we propose a generalized em algorithm to estimate the parameters of the block mixture model and to illustrate our approach we study the case of binary data by using a bernoulli block mixture

some comments on cp i study the typical configuration of a cp plot when the number of variables in the regression problem is large and there are many weak effects i show that a particular configuration that is very commonly seen can arise in a simple way i give a formula by means of which the risk incurred by the minimum cp rule can be estimated

breakdown of interdependent directed networks increasing evidence shows that real world systems interact with one another via dependency connectivities failing connectivities are the mechanism behind the breakdown of interacting complex systems e g blackouts caused by the interdependence of power grids and communication networks previous research analyzing the robustness of interdependent networks has been limited to undirected networks however most real world networks are directed their in degrees and out degrees may be correlated and they are often coupled to one another as interdependent directed networks to understand the breakdown and robustness of interdependent directed networks we develop a theoretical framework based on generating functions and percolation theory we find that for interdependent erd s renyi networks the directionality within each network increases their vulnerability and exhibits hybrid phase transitions we also find that the percolation behavior of interdependent directed scale free networks with and without degree correlations is so complex that two criteria are needed to quantify and compare their robustness the percolation threshold and the integrated size of the giant component during an entire attack process interestingly we find that the in degree and out degree correlations in each network layer increase the robustness of interdependent degree heterogeneous networks that most real networks are but decrease the robustness of interdependent networks with homogeneous degree distribution and with strong coupling strengths moreover by applying our theoretical analysis to real interdependent international trade networks we find that the robustness of these real world systems increases with the in degree and out degree correlations confirming our theoretical analysis

effects of preference for attachment to low degree nodes on the degree distributions of a growing directed network and a simple food web model we study the growth of a directed network in which the growth is constrained by the cost of adding links to the existing nodes we propose a preferential attachment scheme in which a new node attaches to an existing node i with probability ensuremath pi k i ensuremath propto k i ensuremath 1 where k i is the number of outgoing links at i we calculate the degree distribution for the outgoing links in the asymptotic regime t ensuremath rightarrow ensuremath infty n k both analytically and by monte carlo simulations the distribution decays like k ensuremath mu k ensuremath gamma k for large k where ensuremath mu is a constant we investigate the effect of this preferential attachment scheme by comparing the results to an equivalent growth model with a degree independent probability of attachment which gives an exponential outdegree distribution also we relate this mechanism to simple food web models by implementing it in the cascade model we show that the low degree preferential attachment mechanism breaks the symmetry between in and outdegree distributions in the cascade model it also causes a faster decay in the tails of the outdegree distributions for both our network growth model and the cascade model

hierarchy in directed random networks in recent years the theory and application of complex networks have been quickly developing in a markable way due to the increasing amount of data from real systems and the fruitful application of powerful methods used in statistical physics many important characteristics of social or biological systems can be described by the study of their underlying structure of interactions hierarchy is one of these features that can be formulated in the language of networks in this paper we present some qualitative analytic results on the hierarchical properties of random network models with zero correlations and also investigate mainly numerically the effects of different types of correlations the behavior of the hierarchy is different in the absence and the presence of giant components we show that the hierarchical structure can be drastically different if there are one point correlations in the network we also show numerical results suggesting that the hierarchy does not change monotonically with the correlations and there is an optimal level of nonzero correlations maximizing the level of hierarchy

k core architecture and k core percolation on complex networks abstract we analytically describe the architecture of uncorrelated complex networks with arbitrary degree distributions as a set of successively enclosed substructures k cores we find the structure of k cores their sizes and their birth points the bootstrap percolation thresholds we show that in networks with a finite mean number z 2 of the second nearest neighbours the emergence of a k core is a unique hybrid phase transition with a jump emergence of the k core as at a first order phase transition but also with a critical singularity as at a continuous phase transition in contrast if z 2 diverges the networks contain an infinite sequence of k cores which are ultra robust against random damage we explain the meaning of the order parameter for k core percolation and reveal the origin of the specific critical phenomena we demonstrate that a so called corona of the k core plays a crucial role it turns out that the k core percolation threshold is at the same time the percolation threshold of finite corona clusters the mean separation of vertices in corona clusters plays the role of the correlation length and diverges at the critical point we demonstrate that a random removal of even one vertex from the k core may result in the collapse of a vast region of the k core around the removed vertex the mean size of this region diverges at the critical point we find an exact mapping of k core percolation to a model of cooperative relaxation this model undergoes critical relaxation with a divergent rate at some critical moment

is there an optimal forecast combination abstract we consider several geometric approaches for combining forecasts in large samples a simple eigenvector approach a mean corrected eigenvector and trimmed eigenvector approach we give conditions where geometric approach yields identical result as the regression approach we also consider a mean and scale corrected simple average of all predictive models for finite sample and give conditions where simple average is an optimal combination monte carlos are conducted to compare the finite sample performance of these and some popular forecast combination and information combination methods and to shed light on the issues of forecast combination vs information combination we also try to shed light on whether there exists an optimal forecast combination method by comparing various forecast combination methods to predict us real output growth rate and excess equity premium

auto regressive shape classifiers in time varying noise abstract good results have been reported in a number of recent studies where auto regressive shape classifiers have been used to allocate objects to a pre determined number of classes it is shown that this approach may give unsatisfactory results when applied to imaging systems whose noise characteristics vary substantially over time such variation may be due to changes in atmospheric conditions or the relative effect of discretization errors observations are based on the reconstruction of a number of noisy synthetic templates

invariant pattern recognition by moment fourier descriptor abstract this paper proposes a new shape descriptor called moment fourier descriptor which can describe a complex object composed by a set of closed regions this descriptor is shown to be independent of object s translation rotation and scaling the essential advantage of moment fourier descriptor is that it can be used to recognize more complex patterns than the traditional fourier descriptors simulations are given to show the high performance of the proposed approach

3 d to 2 d pose determination with regions this paper presents a novel approach to parts based object recognition in the presence of occlusion we focus on the problem of determining the pose of a 3 d object from a single 2 d image when convex parts of the object have been matched to corresponding regions in the image we consider three types of occlusions self occlusion occlusions whose locus is identified in the image and completely arbitrary occlusions we show that in the first two cases this is a convex optimization problem derive efficient algorithms and characterize their performance for the last case we prove that the problem of finding valid poses is computationally hard but provide an efficient approximate algorithm this work generalizes our previous work on region based object recognition which focused on the case of planar models

on the computational aspects of affine moment invariants for gray scale images affine moment invariants are widely used in pattern recognition and computer vision the affine transform usually decomposed into a set of geometric transforms the computation of these transforms relies on their relations with the geometric moments approximate computation of geometric moments produced a very large dynamic range that results in numerical instabilities in this work an exact method is used for the computation of affine moment invariants for gray level images where the proposed method completely removes the approximation errors numerical experiments are performed to test the invariance of symmetric as well as asymmetric images subject to affine transformations a comparison with the approximation method is made the obtained results clearly explained the efficiency of the proposed method

k core bootstrap percolation on complex networks critical phenomena and nonlocal effects we develop the theory of the k core bootstrap percolation on uncorrelated random networks with arbitrary degree distributions we show that the k core percolation is an unusual hybrid phase transition with a jump emergence of the k core as at a first order phase transition but also with a critical singularity as at a continuous transition we describe the properties of the k core explain the meaning of the order parameter for the k core percolation and reveal the origin of the specific critical phenomena we demonstrate that a so called corona of the k core plays a crucial role corona is a subset of vertices in the k core which have exactly k neighbors in the k core it turns out that the k core percolation threshold is at the same time the percolation threshold of finite corona clusters the mean separation of vertices in corona clusters plays the role of the correlation length and diverges at the critical point we show that a random removal of even one vertex from the k core may result in the collapse of a vast region of the k core around the removed vertex the mean size of this region diverges at the critical point we find an exact mapping of the k core percolation to a model of cooperative relaxation this model undergoes critical relaxation with a divergent rate at some critical moment

fuzzy pyramid based invariant object recognition abstract a new fuzzy decision based invariant pattern recognition method for object recognition is presented blending the well known image pyramid data structure with fuzzy mathematics the new method sets out to show how fuzzy concepts such as membership grade are used in fuzzifying the features and building up a fuzzy feature pyramid this is supported by a new segmentation technique using annular and sector windows inspired by the human visual system a fuzzy pyramid technique is then naturally introduced to make the recognition decision this new fuzzy decision approach is shown to have all the essential invariant properties and robustness against noise and uncertainty the recognition system was applied to the problem of tool recognition and good results were obtained a comparative study reveals many important advantages of our method over other techniques

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

more comments on c p we discuss the interpretation of c p plots and show how they can be calibrated in several ways we comment on the practice of using the display as a basis for formal selection of a subset regression model and extend the range of application of the device to encompass arbitrary linear estimates of the regression coefficients for example ridge estimates

giant strongly connected component of directed networks we describe how to calculate the sizes of all giant connected components of a directed graph including the strongly connected one in particular the world wide web is a directed network the results are obtained for graphs with statistically uncorrelated vertices and an arbitrary joint in and out degree distribution p k i k o we show that if p k i k o does not factorize the relative size of the giant strongly connected component deviates from the product of the relative sizes of the giant in and out components the calculations of the relative sizes of all the giant components are demonstrated using the simplest examples we explain that the giant strongly connected component may be less resilient to random damage than the giant weakly connected one

moment based methods for polygonal approximation of digitized curves object shape representation plays an important role in the area of image processing pattern recognition and computer vision in the past two decades many algorithms have been suggested for creating approximated polygons in this study two new polygonal approximation methods based on the geometric moments and the orthogonal moments defined in terms of legendre polynomials are proposed the difference between the moments defined by the initial contour and those of the approximated polygon is taken as the objective function each algorithm provides various polygonal approximation results with different number of line segments for different application situations for a given error bound we can determine the optimal polygon with a minimum number of line segments the procedures are applied to some digital curves and better results are obtained in comparison with some known methods

biclustering models for two mode ordinal data the work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two mode ordinal categorical response data such as those resulting from likert scale responses we use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data model fitting is performed using the em algorithm and a fuzzy allocation of rows and columns to corresponding clusters is obtained the clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets

network algorithmics and the emergence of information integration in cortical models an information theoretic framework known as integrated information theory iit has been introduced recently for the study of the emergence of consciousness in the brain d balduzzi and g tononi plos comput biol 4 e1000091 2008 iit purports that this phenomenon is to be equated with the generation of information by the brain surpassing the information that the brain s constituents already generate independently of one another iit is not fully plausible in its modeling assumptions nor is it testable due to severe combinatorial growth embedded in its key definitions here we introduce an alternative to iit which while inspired in similar information theoretic principles seeks to address some of iit s shortcomings to some extent our alternative framework uses the same network algorithmic cortical model we introduced earlier a nathan and v c barbosa phys rev e 81 021916 2010 and to allow for somewhat improved testability relative to iit adopts the well known notions of information gain and total correlation applied to a set of variables representing the reachability of neurons by messages in the model s dynamics we argue that these two quantities relate to each other in such a way that can be used to quantify the system s efficiency in generating information beyond that which does not depend on integration we give computational results on our cortical model and on variants thereof that are either structurally random in the sense of an erd s renyi random directed graph or structurally deterministic we have found that our cortical model stands out with respect to the others in the sense that many of its instances are capable of integrating information more efficiently than most of those others instances

reversible jump markov chain monte carlo computation and bayesian model determination markov chain monte carlo methods for bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure they have therefore not been available for application to bayesian model determination where the dimensionality of the parameter vector is typically not fixed this paper proposes a new framework for the construction of reversible markov chain samplers that jump between parameter subspaces of differing dimensionality which is flexible and entirely constructive it should therefore have wide applicability in model determination problems the methodology is illustrated with applications to multiple change point analysis in one and two dimensions and to a bayesian comparison of binomial experiments

invariant fitting of planar objects by primitives the determination of invariant characteristics is an important problem in pattern recognition many invariants are known which have been obtained by the method of normalization in this paper we introduce a new approach of fitting planar objects by primitives using the method of normalization e g fitting by lines triangles rectangles circles ellipses super quadrics etc objects and primitives are described by features for example by moments the main advantage is that the normalization process provides us with a canonical frame of the object and the primitive therefore the fit is invariant with respect to the transformation used by this new method an analytical fitting of non analytical objects can be achieved for example fitting by polygons furthermore the numerical effort can be reduced drastically by normalizing of the object and the primitive

interfaces and the edge percolation map of random directed networks the traditional node percolation map of directed networks is reanalyzed in terms of edges in the percolated phase edges can mainly organize into five distinct giant connected components interfaces bridging the communication of nodes in the strongly connected component and those in the in and out components formal equations for the relative sizes in the number of edges of these giant structures are derived for arbitrary joint degree distributions in the presence of local and two point correlations the uncorrelated null model is fully solved analytically and compared against simulations finding an excellent agreement interfaces and their particular conformations giving place from hairy ball percolation landscapes to bottleneck straits could bring new light to the discussion of how a structure is interwoven with functionality in flow networks

an optimal selection of regression variables summary an asymptotically optimal selection of regression variables is proposed the key assumption is that the number of control variables is infinite or increases with the sample size it is also shown that mallows s qp akaike s fpe and aic methods are all asymptotically equivalent to this method

the visual hull concept for silhouette based image understanding many algorithms for both identifying and reconstructing a 3 d object are based on the 2 d silhouettes of the object in general identifying a nonconvex object using a silhouette based approach implies neglecting some features of its surface as identification clues the same features cannot be reconstructed by volume intersection techniques using multiple silhouettes of the object this paper addresses the problem of finding which parts of a nonconvex object are relevant for silhouette based image understanding for this purpose the geometric concept of visual hull of a 3 d object is introduced this is the closest approximation of object s that can be obtained with the volume intersection approach it is the maximal object silhouette equivalent to s i e which can be substituted for s without affecting any silhouette only the parts of the surface of s that also lie on the surface of the visual hull can be reconstructed or identified using silhouette based algorithms the visual hull depends not only on the object but also on the region allowed to the viewpoint two main viewing regions result in the external and internal visual hull in the former case the viewing region is related to the convex hull of s in the latter it is bounded by s the internal visual hull also admits an interpretation not related to silhouettes algorithms for computing visual hulls are presented and their complexity analyzed in general the visual hull of a 3 d planar face object turns out to be bounded by planar and curved patches

second look at the spread of epidemics on networks in an important paper newman phys rev e66 016128 2002 claimed that a general network based stochastic susceptible infectious removed sir epidemic model is isomorphic to a bond percolation model where the bonds are the edges of the contact network and the bond occupation probability is equal to the marginal probability of transmission from an infected node to a susceptible neighbor in this paper we show that this isomorphism is incorrect and define a semidirected random network we call the epidemic percolation network that is exactly isomorphic to the sir epidemic model in any finite population in the limit of a large population i the distribution of self limited outbreak sizes is identical to the size distribution of small out components ii the epidemic threshold corresponds to the phase transition where a giant strongly connected component appears iii the probability of a large epidemic is equal to the probability that an initial infection occurs in the giant in component and iv the relative final size of an epidemic is equal to the proportion of the network contained in the giant out component for the sir model considered by newman we show that the epidemic percolation network predicts the same mean outbreak size below the epidemic threshold the same epidemic threshold and the same final size of an epidemic as the bond percolation model however the bond percolation model fails to predict the correct outbreak size distribution and probability of an epidemic when there is a nondegenerate infectious period distribution we confirm our findings by comparing predictions from percolation networks and bond percolation models to the results of simulations in the appendix we show that an isomorphism to an epidemic percolation network can be defined for any time homogeneous stochastic sir model

early fragmentation in the adaptive voter model on directed networks we consider voter dynamics on a directed adaptive network with fixed out degree distribution a transition between an active phase and a fragmented phase is observed this transition is similar to the undirected case if the networks are sufficiently dense and have a narrow out degree distribution however if a significant number of nodes with low out degree is present then fragmentation can occur even far below the estimated critical point due to the formation of self stabilizing structures that nucleate fragmentation this process may be relevant for fragmentation in current political opinion formation processes

analysis of complex contagions in random multiplex networks we study the diffusion of influence in random multiplex networks where links can be of r different types and for a given content e g rumor product or political view each link type is associated with a content dependent parameter ci in 0 that measures the relative bias type i links have in spreading this content in this setting we propose a linear threshold model of contagion where nodes switch state if their perceived proportion of active neighbors exceeds a threshold namely a node connected to mi active neighbors and ki mi inactive neighbors via type i links will turn active if cimi ciki exceeds its threshold under this model we obtain the condition probability and expected size of global spreading events our results extend the existing work on complex contagions in several directions by i providing solutions for coupled random networks whose vertices are neither identical nor disjoint ii highlighting the effect of content on the dynamics of complex contagions and iii showing that content dependent propagation over a multiplex network leads to a subtle relation between the giant vulnerable component of the graph and the global cascade condition that is not seen in the existing models in the literature

generalized percolation in random directed networks we develop a general theory for percolation in directed random networks with arbitrary two point correlations and bidirectional edges that is edges pointing in both directions simultaneously these two ingredients alter the previously known scenario and open new views and perspectives on percolation phenomena equations for the percolation threshold and the sizes of the giant components are derived in the most general case we also present simulation results for a particular example of uncorrelated network with bidirectional edges confirming the theoretical predictions

thesaurus as a complex network abstract a thesaurus is one out of many possible representations of term or word connectivity the terms of a thesaurus are seen as the nodes and their relationship as the links of a directed graph the directionality of the links retains all the thesaurus information and allows the measurement of several quantities this has lead to a new term classification according to the characteristics of the nodes for example nodes with no links in no links out etc using an electronic available thesaurus we have obtained the incoming and outgoing link distributions while the incoming link distribution follows a stretched exponential function the lower bound for the outgoing link distribution has the same envelope of the scientific paper citation distribution proposed by tsallis and albuquerque eur phys j b 13 2000 777 however a better fit is obtained by simpler function which is the solution of ricatti s differential equation we conjecture that this differential equation is the continuous limit of a stochastic growth model of the thesaurus network we also propose a new manner to arrange a thesaurus using the inversion method

combining color and shape information for illumination viewpoint invariant object recognition in this paper we propose a new scheme that merges color and shape invariant information for object recognition to obtain robustness against photometric changes color invariant derivatives are computed first color invariance is an important aspect of any object recognition scheme as color changes considerably with the variation in illumination object pose and camera viewpoint these color invariant derivatives are then used to obtain similarity invariant shape descriptors shape invariance is equally important as under a change in camera viewpoint and object pose the shape of a rigid object undergoes a perspective projection on the image plane then the color and shape invariants are combined in a multidimensional color shape context which is subsequently used as an index as the indexing scheme makes use of a color shape invariant context it provides a high discriminative information cue robust against varying imaging conditions the matching function of the color shape context allows for fast recognition even in the presence of object occlusion and cluttering from the experimental results it is shown that the method recognizes rigid objects with high accuracy in 3 d complex scenes and is robust against changing illumination camera viewpoint object pose and noise