a fluid analysis framework for a markovian process algebra markovian process algebras such as pepa and stochastic p calculus bring a powerful compositional approach to the performance modelling of complex systems however the models generated by process algebras as with other interleaving formalisms are susceptible to the state space explosion problem models with only a modest number of process algebra terms can easily generate so many states that they are all but intractable to traditional solution techniques previous work aimed at addressing this problem has presented a fluid flow approximation allowing the analysis of systems which would otherwise be inaccessible to achieve this systems of ordinary differential equations describing the fluid flow of the stochastic process algebra model are generated informally in this paper we show formally that for a large class of models this fluid flow analysis can be directly derived from the stochastic process algebra model as an approximation to the mean number of component types within the model the nature of the fluid approximation is derived and characterised by direct comparison with the chapman kolmogorov equations underlying the markov model furthermore we compare the fluid approximation with the exact solution using stochastic simulation and we are able to demonstrate that it is a very accurate approximation in many cases for the first time we also show how to extend these techniques naturally to generate systems of differential equations approximating higher order moments of model component counts these are important performance characteristics for estimating for instance the variance of the component counts this is very necessary if we are to understand how precise the fluid flow calculation is in a given modelling situation

algorithmic product form approximations of interacting stochastic models a large variety of product form solutions for continuous time markovian models can be derived by checking a set of structural properties of the underlying stochastic processes and a condition on their reversed rates in previous work marin and vigliotti 2010 9 we have shown how to derive a large class of product form solutions using a different formulation of the reversed compound agent theorem grcat we continue this line of work by showing that it is possible to exploit this result to approximate the steady state distribution of non product form model interactions by means of product form ones

analysis of constrained damping layers including normal strain effects a beam partially covered with a constrained damping layer is analyzed the longitudinal normal strain and shear strain in the viscoelastic layer are considered hamilton s principle is used to derive equations of motion and boundary conditions a sixth order equation is used to describe the portion of the beam covered with a constrained damping layer the characteristic equations are solved numerically to determine normalized natural frequency and loss factor values the loss parameter normalized loss factor is shown to be a function of the shear parameter gr the geometric parameter y t the normalized coverage length l c l dimensionless coefficients b 1 b 2 and b 3 and the core loss factor n 2 it is shown that traditional constrained layer damping theory that includes only shearing in the damping layer is inadequate for very small or very large values of the shear parameter optimal shear parameter and coverage length values are determined for a variety of boundary conditions results demonstrate the optimal values for the shear parameter and coverage length to obtain maximum damping levels plots are derived to determine the optimal coverage length for any shear parameter value for maximum loss factor

principal warps thin plate splines and the decomposition of deformations the decomposition of deformations by principal warps is demonstrated the method is extended to deal with curving edges between landmarks this formulation is related to other applications of splines current in computer vision how they might aid in the extraction of features for analysis comparison and diagnosis of biological and medical images in indicated

a graduated assignment algorithm for graph matching a graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise by combining graduated nonconvexity two way assignment constraints and sparsity large improvements in accuracy and speed are achieved its low order computational complexity o lm where l and m are the number of links in the two graphs and robustness in the presence of noise offer advantages over traditional combinatorial approaches the algorithm not restricted to any special class of graph is applied to subgraph isomorphism weighted graph matching and attributed relational graph matching to illustrate the performance of the algorithm attributed relational graphs derived from objects are matched then results from twenty five thousand experiments conducted on 100 mode random graphs of varying types graphs with only zero one links weighted graphs and graphs with node attributes and multiple link types are reported no comparable results have been reported by any other graph matching algorithm before in the research literature twenty five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated

bayesian graph edit distance this paper describes a novel framework for comparing and matching corrupted relational graphs the paper develops the idea of edit distance originally introduced for graph matching by sanfeliu and fu 1983 we show how the levenshtein distance 1966 can be used to model the probability distribution for structural errors in the graph matching problem this probability distribution is used to locate matches using map label updates we compare the resulting graph matching algorithm with that recently reported by wilson and hancock the use of edit distance offers an elegant alternative to the exhaustive compilation of label dictionaries moreover the method is polynomial rather than exponential in its worst case complexity we support our approach with an experimental study on synthetic data and illustrate its effectiveness on an uncalibrated stereo correspondence problem this demonstrates experimentally that the gain in efficiency is not at the expense of quality of match

comparing images using the hausdorff distance the hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa thus this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another efficient algorithms for computing the hausdorff distance between all possible relative positions of a binary image and a model are presented the focus is primarily on the case in which the model is only allowed to translate with respect to the image the techniques are extended to rigid motion the hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived the method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods it is shown that the method extends naturally to the problem of comparing a portion of a model against an image

on the approximability of numerical taxonomy fitting distances by tree metrics we consider the problem of fitting an n n distance matrix d by a tree metric t let varepsilon be the distance to the closest tree metric under the l infty norm that is varepsilon min t parallel t d parallel infty first we present an o n2 algorithm for finding a tree metric t such that parallel t d parallel infty leq 3 varepsilon second we show that it is cal np hard to find a tree metric t such that parallel t d parallel infty frac 9 8 varepsilon this paper presents the first algorithm for this problem with a performance guarantee

regularization theory and neural networks architectures we had previously shown that regularization principles lead to approximation schemes that are equivalent to networks with one layer of hidden units called regularization networks in particular s

robustness of shape descriptors to incomplete contour representations with inspiration from psychophysical researches of the human visual system we propose a novel aspect and a method for performance evaluation of contour based shape recognition algorithms regarding their robustness to incompleteness of contours we use complete contour representations of objects as a reference training set incomplete contour representations of the same objects are used as a test set the performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained we call this evaluation procedure the icr test we consider three types of contour incompleteness viz segment wise contour deletion occlusion and random pixel depletion as an illustration the robustness of two shape recognition algorithms to contour incompleteness is evaluated these algorithms use a shape context and a distance multiset as local shape descriptors qualitatively both algorithms mimic human visual perception in the sense that recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours the distance multiset method performs better than the shape context method in this test framework

lumping based equivalences in markovian automata algorithms and applications to product form analyses abstract in this paper we consider two relations over stochastic automata named lumpable bisimulation and exact equivalence that induce a strong and an exact lumping respectively on the underlying markov chains we show that an exact equivalence over the states of a non synchronising automaton is indeed a lumpable bisimulation for the corresponding reversed automaton and then it induces a strong lumping on the time reversed markov chain underlying the model this property allows us to prove that the class of quasi reversible models is closed under exact equivalence quasi reversibility is a pivotal property to study product form models hence exact equivalence turns out to be a theoretical tool to prove the product form of models by showing that they are exactly equivalent to models which are known to be quasi reversible algorithms for computing both lumpable bisimulation and exact equivalence are introduced case studies as well as performance tests are also presented

on the hardness of inferring phylogenies from triplet dissimilarities this work considers the problem of reconstructing a phylogenetic tree from triplet dissimilarities which are dissimilarities defined over taxon triplets triplet dissimilarities are possibly the simplest generalization of pairwise dissimilarities and were used for phylogenetic reconstructions in the past few years we study the hardness of finding a tree best fitting a given triplet dissimilarity table under the norm we show that the corresponding decision problem is np hard and that the corresponding optimization problem cannot be approximated in polynomial time within a constant multiplicative factor smaller than 1 4 on the positive side we present a polynomial time constant rate approximation algorithm for this problem we also address the issue of best fit under maximal distortion which corresponds to the largest ratio between matching entries in two triplet dissimilarity tables we show that it is np hard to approximate the corresponding optimization problem within any constant multiplicative factor

new algorithms for 2d and 3d point matching pose estimation and correspondence abstract a fundamental open problem in computer vision determining pose and correspondence between two sets of points in space is solved with a novel fast robust and easily implementable algorithm the technique works on noisy 2d or 3d point sets that may be of unequal sizes and may differ by non rigid transformations using a combination of optimization techniques such as deterministic annealing and the softassign which have recently emerged out of the recurrent neural network statistical physics framework analog objective functions describing the problems are minimized over thirty thousand experiments on randomly generated points sets with varying amounts of noise and missing and spurious points and on hand written character sets demonstrate the robustness of the algorithm

a constructive algorithm for realizing a distance matrix the natural metric of a weighted graph is the length of the shortest paths between all pairs of vertices the investigated problem consists in a representation of a given metric by a graph such that the total length of the graph is minimized for that purpose we give a constructive algorithm based on a technique of reduction fusion and deletion we then show some results on a set of various distance matrices whose optimal realization is known

modal matching for correspondence and recognition modal matching is a new method for establishing correspondences and computing canonical descriptions the method is based on the idea of describing objects in terms of generalized symmetries as defined by each object s eigenmodes the resulting modal description is used for object recognition and categorization where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects in general modes provide a global to local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison in contrast to previous techniques which required correspondence to be computed with an initial or prototype shape modal matching utilizes a new type of finite element formulation that allows for an object s eigenmodes to be computed directly from available image information this improved formulation provides greater generality and accuracy and is applicable to data of any dimensionality correspondence results with 2d contour and point feature data are shown and recognition experiments with 2d images of hand tools and airplanes are described

efficient algorithms for inverting evolution evolution can be mathematically modelled by a stochastic process that operates on the dna of species such models are based on the established theory that the dna sequences or genomes of all extant species have been derived from the genome of the common ancestor of all species by a process of random mutation and natural selection a stochastic model of evolution can be used to construct phylogenies or evolutionary trees for a set of species maximum likelihood estimation mle methods seek the evolutionary tree which is most likely to have produced the dna under consideration while these methods are intellectually satisfying they have not been widely accepted because of their computational intractability in this paper we address the intractability of mle methods as follows we introduce a metric on stochastic process models of evolution we show that this metric is meaningful by proving that in order for any algorithm to distinguish between two stochastic models that are close according to this metric it needs to be given many observations we complement this result with a simple and efficient algorithm for inverting the stochastic process of evolution that is for building a tree from observations on two state characters we will use the same techniques in a subsequent paper to solve the problem for multistate characters and hence for building a tree from dna sequence data the tree we build is provably close in our metric to the tree generating the data and gets closer as more observations become available though there have been many heuristics suggested for the problem of finding good approximations to the most likely tree our algorithm is the first one with a guaranteed convergence rate and further this rate is within a polynomial of the lower bound rate we establish ours is also the first polynomial time algorithm that is proven to converge at all to the correct tree

the earth mover s distance as a metric for image retrieval we investigate the properties of a metric between two distributions the earth mover s distance emd for content based image retrieval the emd is based on the minimal cost that must be paid to transform one distribution into the other in a precise sense and was first proposed for certain vision problems by peleg werman and rom for image retrieval we combine this idea with a representation scheme for distributions that is based on vector quantization this combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods the emd is based on a solution to the transportation problem from linear optimization for which efficient algorithms are available and also allows naturally for partial matching it is more robust than histogram matching techniques in that it can operate on variable length representations of the distributions that avoid quantization and other binning problems typical of histograms when used to compare distributions with the same overall mass the emd is a true metric in this paper we focus on applications to color and texture and we compare the retrieval performance of the emd with that of other distances

learning a probabilistic topology discovering model for scene categorization a recent advance in scene categorization prefers a topological based modeling to capture the existence and relationships among different scene components to that effect local features are typically used to handle photographing variances such as occlusions and clutters however in many cases the local features alone cannot well capture the scene semantics since they are extracted from tiny regions e g 4 times 4 patches within an image in this paper we mine a discriminative topology and a low redundant topology from the local descriptors under a probabilistic perspective which are further integrated into a boosting framework for scene categorization in particular by decomposing a scene image into basic components a graphlet model is used to describe their spatial interactions accordingly scene categorization is formulated as an intergraphlet matching problem the above procedure is further accelerated by introducing a probabilistic based representative topology selection scheme that makes the pairwise graphlet comparison trackable despite their exponentially increasing volumes the selected graphlets are highly discriminative and independent characterizing the topological characteristics of scene images a weak learner is subsequently trained for each topology which are boosted together to jointly describe the scene image in our experiment the visualized graphlets demonstrate that the mined topological patterns are representative to scene categories and our proposed method beats state of the art models on five popular scene data sets

skeletal shape abstraction from examples learning a class prototype from a set of exemplars is an important challenge facing researchers in object categorization although the problem is receiving growing interest most approaches assume a one to one correspondence among local features restricting their ability to learn true abstractions of a shape in this paper we present a new technique for learning an abstract shape prototype from a set of exemplars whose features are in many to many correspondence focusing on the domain of 2d shape we represent a silhouette as a medial axis graph whose nodes correspond to partsrdquo defined by medial branches and whose edges connect adjacent parts given a pair of medial axis graphs we establish a many to many correspondence between their nodes to find correspondences among articulating parts based on these correspondences we recover the abstracted medial axis graph along with the positional and radial attributes associated with its nodes we evaluate the abstracted prototypes in the context of a recognition task

turning back time in markovian process algebra product form solutions in markovian process algebra mpa are constructed using properties of reversed processes the compositionality of mpas is directly exploited allowing a large class of hierarchically constructed systems to be solved for their state probabilities at equilibrium the paper contains new results on both reversed stationary markov processes as well as mpa itself and includes a mechanisable proof in mpa notation of jackson s theorem for product form queueing networks several examples are used to illustrate the approach

poisson arrivals see time averages in many stochastic models particularly in queueing theory poisson arrivals both observe see a stochastic process and interact with it in particular cases and or under restrictive assumptions it has been shown that the fraction of arrivals that see the process in some state is equal to the fraction of time the process is in that state in this paper we present a proof of this result under one basic assumption the process being observed cannot anticipate the future jumps of the poisson process

multilevel splitting for estimating rare event probabilities we analyze the performance of a splitting technique for the estimation of rare event probabilities by simulation a straightforward estimator of the probability of an event evaluates the proportion of simulated paths on which the event occurs if the event is rare even a large number of paths may produce little information about its probability using this approach the method we study reinforces promising paths at intermediate thresholds by splitting them into subpaths which then evolve independently if implemented appropriately this has the effect of dedicating a greater fraction of the computational effort to informative runs we analyze the method for a class of models in which roughly speaking the number of states through which each threshold can be crossed is bounded under additional assumptions we identify the optimal degree of splitting at each threshold as the rarity of the event increases it should be set so that the expected number of subpaths reaching each threshold remains roughly constant thus implemented the method is provably effective in a sense appropriate to rare event simulations these results follow from a branching process analysis of the method we illustrate our theoretical results with some numerical examples for queueing models

shear and normal strain effects of core layers in vibration of square sandwich plates under clamped boundary conditions the authors previously investigated the effects of both shear and normal strains in the viscoelastic core layer on modal properties of sandwich plates under hinged boundary conditions for such boundary conditions analytic formulation of characteristic equations was possible agreeing that most real boundary conditions must lie somewhere between hinged and clamped it is naturally worthwhile to solve for the clamped boundary conditions for which however analytic formulation of the characteristic equations is not possible in this study hence the finite difference method is utilized as a tool for numerical analysis the effects of neglecting the extensional or compressional strain in the core material for the clamped boundary conditions are studied and compared with the case of simply supported boundary conditions

random neural networks with negative and positive signals and product form solution we introduce a new class of random neural networks in which signals are either negative or positive a positive signal arriving at a neuron increases its total signal count or potential by one a negative signal reduces it by one if the potential is positive and has no effect if it is zero when its potential is positive a neuron fires sending positive or negative signals at random intervals to neurons or to the outside positive signals represent excitatory signals and negative signals represent inhibition we show that this model with exponential signal emission intervals poisson external signal arrivals and markovian signal movements between neurons has a product form leading to simple analytical expressions for the system state

snapshots a novel local surface descriptor and matching algorithm for robust 3d surface alignment in this paper a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3d object the descriptor is based on taking snapshots of the surface over each point using a virtual camera oriented perpendicularly to the surface this representation has the advantage of imposing minimal loss of information be robust to self occlusions and also be very efficient to compute then we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap

rare event restart simulation of two stage networks abstract repetitive simulation trials after reaching thresholds restart is a widely applied accelerated simulation technique that allows the evaluation of extremely low probabilities in this method a number of simulation retrials are performed when the process enters regions of the state space where the chance of occurrence of the rare event is higher formulas for evaluating the optimal number of regions and retrials as well as guidelines for obtaining suitable importance functions were provided in previous papers nevertheless further investigations are required to apply these guidelines to practical cases in this paper we obtain efficient importance functions for networks with two stages and different nodes in each stage some approximations are used to derive the formulas the goodness of such approximations is supported by the simulation results obtained the methodology applied can be extended to other networks

a tutorial on empa a theory of concurrent processes with nondeterminism priorities probabilities and time we give an overview of empa a process algebra for modeling and analyzing concurrent systems where nondeterminism priorities probabilities and time are present aiming at a reasonable trade off between the expressive power of the calculus and the complexity of its underlying theory

object recognition as many to many feature matching object recognition can be formulated as matching image features to model features when recognition is exemplar based feature correspondence is one to one however segmentation errors articulation scale difference and within class deformation can yield image and model features which don t match one to one but rather many to many adopting a graph based representation of a set of features we present a matching algorithm that establishes many to many correspondences between the nodes of two noisy vertex labeled weighted graphs our approach reduces the problem of many to many matching of weighted graphs to that of many to many matching of weighted point sets in a normed vector space this is accomplished by embedding the initial weighted graphs into a normed vector space with low distortion using a novel embedding technique based on a spherical encoding of graph structure many to many vector correspondences established by the earth mover s distance framework are mapped back into many to many correspondences between graph nodes empirical evaluation of the algorithm on an extensive set of recognition trials including a comparison with two competing graph matching approaches demonstrates both the robustness and efficacy of the overall approach

vibration and dynamic stability of a traveling sandwich beam the vibration and dynamic stability of a traveling sandwich beam are studied using the finite element method the damping layer is assumed to be linear viscoelastic and almost incompressible the extensional and shear moduli of the viscoelastic material are characterized by complex quantities complex eigenvalue problems are solved by the state space method and the natural frequencies and modal loss factors of the composite beam are extracted the effects of stiffness and thickness ratio of the viscoelastic and constrained layers on natural frequencies and modal loss factors are reported tension fluctuations are the dominant source of excitation in a traveling sandwich material and the regions of dynamic instability are determined by modified bolotin s method numerical results show that the constrained damping layer stabilizes the traveling sandwich beam

3d shape retrieval using kernels on extended reeb graphs abstract 3d shape retrieval is becoming an acute issue for numerous applications that span from cad to serious games to biomedicine and all contexts where it is fundamental to automatically retrieve geometric information from a collection of 3d models this paper addresses 3d shape retrieval in terms of a graph based description and the definition of a corresponding similarity measure for this purpose 3d models are represented as bags of shortest paths defined over well chosen extended reeb graphs while the similarity between pairs of extended reeb graphs is addressed through kernels adapted to these descriptions results are comparable with the best results of the literature and the modularity and evolutivity of the method ensure its applicability to other problems from partial shape matching to classification

an approach to the parameterization of structure for fast categorization a decomposition is described which parameterizes the geometry and appearance of contours and regions of gray scale images with the goal of fast categorization to express the contour geometry a contour is transformed into a local global space from which parameters are derived classifying its global geometry arc inflexion or alternating and describing its local aspects degree of curvature edginess symmetry regions are parameterized based on their symmetric axes which are evolved with a wave propagation process enabling to generate the distance map for fragmented contour images the methodology is evaluated on three image sets the caltech 101 set and two sets drawn from the corel collection the performance nearly reaches the one of other categorization systems for unsupervised learning

size functions for comparing 3d models this paper proposes an original framework to use size functions in the 3d context size functions are a mathematical tool that have already shown its effectiveness for image retrieval and classification they are introduced here for the first time to discriminate among 3d objects represented by triangle meshes through the proposal of a method for defining size graphs independently of the underlying triangulation we first derive a skeletal signature which guarantees the topological coding and the geometric description of an object surface then this signature is used as a size graph to compute discrete size functions the attractive feature of size functions is that it readily gives a similarity measure between shapes the result is the introduction of a new technique for 3d model retrieval devised to capture both local and global properties of a shape finally we demonstrate the potential of our approach in a set of experiments and discuss the results with respect to existing techniques

stability of the random neural network model in a recent paper 1 we have introduced a new neural network model called the random network in which negative or positive signals circulate modelling inhibitory and excitatory signals they are summed at the input of each neuron and constitute its signal potential the state of each neuron in this model is its signal potential while the network state is the vector of signal potentials at each neuron if its potential is positive a neuron fires and sends out signals to the other neurons of the network or to the outside world as it does so its signal potential is depleted we have shown that in the markovian case this model has product form i e the steady state probability distribution of its potential vector is the product of the marginal probabilities of the potential at each neuron the signal flow equations of the network which describe the rate at which positive or negative signals arrive to each neuron are non linear so that their existence and uniqueness is not easily established except for the case of feedforward or backpropagation networks 1 we examine two sub classes of networks balanced and damped networks and obtain stability conditions in each case a hardware implementation of these networks is also suggested

exact fluid lumpability in markovian process algebra quantitative analysis by means of discrete state stochastic processes is hindered by the well known phenomenon of state space explosion whereby the size of the state space may have an exponential growth with the number of agents of the system under scrutiny when the stochastic process underlies a markovian process algebra model this problem may be alleviated by suitable notions of behavioural equivalence that induce lumping at the underlying continuous time markov chain establishing an exact relation between a potentially much smaller aggregated chain and the original one for the analysis of massively parallel systems however lumping techniques may not be sufficient to yield a computationally tractable problem recently much work has been directed towards forms of fluid techniques that provide a set of ordinary differential equations odes approximating the expected path of the stochastic process unfortunately even fluid models of realistic systems may be too large for feasible analysis this paper studies a behavioural relation for process algebra with fluid semantics called projected label equivalence which is shown to yield an exactly fluid lumpable model i e an aggregated ode system which can be related to the original one without any loss of information project label equivalence relates sequential components of a process term in general for any two sequential components that are related in the fluid sense nothing can be said about their relationship from the stochastic viewpoint we define and study a notion of well posedness which allows us to relate fluid lumpability to the stochastic notion of semi isomorphism which is a weaker version of the common notion of isomorphism between the doubly labelled transition systems at the basis of the markovian interpretation

an eigendecomposition approach to weighted graph matching problems an approximate solution to the weighted graph matching problem is discussed for both undirected and directed graphs the weighted graph matching problem is that of finding the optimum matching between two weighted graphs which are graphs with weights at each arc the proposed method uses an analytic instead of a combinatorial or iterative approach to the optimum matching problem using the eigendecompositions of the adjacency matrices in the case of the undirected graph matching problem or hermitian matrices derived from the adjacency matrices in the case of the directed graph matching problem a matching close to the optimum can be found efficiently when the graphs are sufficiently close to each other simulation results are given to evaluate the performance of the proposed method

shape clustering this paper aims to address the problem of shape clustering by discovering the common structure which captures the intrinsic structural information of shapes belonging to the same cluster it is based on a skeleton graph named common structure skeleton graph cssg which expresses possible correspondences between nodes of the individual skeletons of the cluster to construct the cssg we derive the correspondences by the optimal subsequence bijection osb to cluster the shape data we apply an agglomerative clustering scheme in each iteration the cssgs are formed from each cluster and the two closest clusters are merged into one the proposed agglomerative clustering algorithm has been evaluated on several shape data sets including three articulated shape data sets torsello s data set and a gesture data set in all experiments our method demonstrates effective performance compared to other algorithms highlights a method is proposed to discover the common structure of a cluster of shapes a cluster of shapes are represented by their cssg assigning weights to nodes and edges in cssgs to help measure their distances an agglomerative strategy is proposed for shape clustering based on cssgs

exact solution for random walks on the triangular lattice with absorbing boundaries the problem of a random walk on a finite triangular lattice with a single interior source point and zig zag absorbing boundaries is solved exactly this problem has been previously considered intractable

path similarity skeleton graph matching this paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes the main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints in contrast to typical tree or graph matching methods we do not consider the topological graph structure our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures the proposed comparison of geodesic paths between endpoints of skeleton graphs yields correct matching results in such cases the skeletons are pruned by contour partitioning with discrete curve evolution which implies that the endpoints of skeleton branches correspond to visual parts of the objects the experimental results demonstrate that our method is able to produce correct results in the presence of articulations stretching and contour deformations

separable solutions for markov processes in random environments abstract in this paper we address the problem of efficiently deriving the steady state distribution for a continuous time markov chain ctmc s evolving in a random environment e the process underlying e is also a ctmc s is called markov modulated process markov modulated processes have been widely studied in literature since they are applicable when an environment influences the behaviour of a system for instance this is the case of a wireless link whose quality may depend on the state of some random factors such as the intensity of the noise in the environment in this paper we study the class of markov modulated processes which exhibits separable product form stationary distribution we show that several models that have been proposed in literature can be studied applying the extended reversed compound agent theorem ercat and also new product forms are derived we also address the problem of the necessity of ercat for product forms and show a meaningful example of product form not derivable via ercat

separable equilibrium state probabilities via time reversal in markovian process algebra the reversed compound agent theorem rcat is a compositional result that uses markovian process algebra mpa to derive the reversed process of certain interactions between two continuous time markov chains at equilibrium from this reversed process together with the given forward process the joint state probabilities can be expressed as a product form although no general algorithm has previously been given this paper first generalises rcat to multiple more than two cooperating agents which removes the need for multiple applications and inductive proofs in cooperations of an arbitrary number of processes a new result shows a simple stochastic equivalence between cooperating synchronised processes and corresponding parallel asynchronous processes this greatly simplifies the proof of the new multi agent theorem which includes a statement of the desired product form solution itself as a product of given state probabilities in the parallel components the reversed process and product form thus derived rely on a solution to certain rate equations and it is shown for the first time that a unique solution exists under mild conditions certainly for queueing networks and g networks

upgma and the normalized equidistant minimum evolution problem upgma unweighted pair group method with arithmetic mean is a widely used clustering method here we show that upgma is a greedy heuristic for the normalized equidistant minimum evolution neme problem that is finding a rooted tree that minimizes the minimum evolution score relative to the dissimilarity matrix among all rooted trees with the same leaf set in which all leaves have the same distance to the root we prove that the neme problem is np hard in addition we present some heuristic and approximation algorithms for solving the neme problem including a polynomial time algorithm that yields a binary rooted tree whose neme score is within o log2n of the optimum

correspondence matching with modal clusters the modal correspondence method of shapiro and brady aims to match point sets by comparing the eigenvectors of a pairwise point proximity matrix although elegant by means of its matrix representation the method is notoriously susceptible to differences in the relational structure of the point sets under consideration in this paper we demonstrate how the method can be rendered robust to structural differences by adopting a hierarchical approach to do this we place the modal matching problem in a probabilistic setting in which the correspondences between pairwise clusters can be used to constrain the individual point correspondences we demonstrate the utility of the method on a number of synthetic and real world point pattern matching problems

multiple instance subspace learning via partial random projection tree for local reflection symmetry in natural images local reflection symmetry detection in nature images is a quite important but challenging task in computer vision the main obstacle is both the scales and the orientations of symmetric structure are unknown the multiple instance learning mil framework sheds lights onto this task owing to its capability to well accommodate the unknown scales and orientations of the symmetric structures however to differentiate symmetry vs non symmetry remains to face extreme confusions caused by clutters scenes and ambiguous object structures in this paper we propose a novel multiple instance learning framework for local reflection symmetry detection named multiple instance subspace learning misl which instead learns a group of models respectively on well partitioned subspaces to obtain such subspaces we propose an efficient dividing strategy under mil setting named partial random projection tree prpt by taking advantage of the fact that each sample bag is represented by the proposed symmetry features computed at specific scale and orientation combinations instances encouraging experimental results on two datasets demonstrate that the proposed local reflection symmetry detection method outperforms current state of the arts highlightswe perform clustering on samples represented by multiple instances we learn a group of mil classifiers on subspaces we report state of the arts results on the symmetry detection benchmark

joint induction of shape features and tree classifiers we introduce a very large family of binary features for two dimensional shapes the salient ones for separating particular shapes are determined by inductive learning during the construction of classification trees there is a feature for every possible geometric arrangement of local topographic codes the arrangements express coarse constraints on relative angles and distances among the code locations and are nearly invariant to substantial affine and nonlinear deformations they are also partially ordered which makes it possible to narrow the search for informative ones at each node of the tree different trees correspond to different aspects of shape they are statistically and weakly dependent due to randomization and are aggregated in a simple way adapting the algorithm to a shape family is then fully automatic once training samples are provided as an illustration we classified handwritten digits from the nist database the error rate was 0 7 percent

importance functions for restart simulation of general jackson networks abstract restart is an accelerated simulation technique that allows the evaluation of extremely low probabilities in this method a number of simulation retrials are performed when the process enters regions of the state space where the chance of occurrence of the rare event is higher these regions are defined by means of a function of the system state called the importance function guidelines for obtaining suitable importance functions and formulas for the importance function of two stage networks were provided in previous papers in this paper we obtain effective importance functions for restart simulation of jackson networks where the rare set is defined as the number of customers in a particular target node exceeding a predefined threshold although some rough approximations and assumptions are used to derive the formulas of the importance functions they are good enough to estimate accurately very low probabilities for different network topologies within short computational time

shape matching and object recognition using shape contexts we present a novel approach to measuring similarity between shapes and exploit it for object recognition in our framework the measurement of similarity is preceded by 1 solving for correspondences between points on the two shapes 2 using the correspondences to estimate an aligning transform in order to solve the correspondence problem we attach a descriptor the shape context to each point the shape context at a reference point captures the distribution of the remaining points relative to it thus offering a globally discriminative characterization corresponding points on two similar shapes will have similar shape contexts enabling us to solve for correspondences as an optimal assignment problem given the point correspondences we estimate the transformation that best aligns the two shapes regularized thin plate splines provide a flexible class of transformation maps for this purpose the dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points together with a term measuring the magnitude of the aligning transform we treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image results are presented for silhouettes trademarks handwritten digits and the coil data set

product form stationary distributions for the m g k group arrival group departure loss system under a general acceptance policy in this paper we investigate under what circumstances the stationary distribution of the numbers of groups of various sizes in the m g k group arrival group departure loss system under a quite general acceptance policy can be obtained in a closed product form also an algorithm is provided for computing the stationary distribution of the number of customers in the system

image categorization by learning a propagated graphlet path spatial pyramid matching is a standard architecture for categorical image retrieval however its performance is largely limited by the prespecified rectangular spatial regions when pooling local descriptors in this paper we propose to learn object shaped and directional receptive fields for image categorization in particular different objects in an image are seamlessly constructed by superpixels while the direction captures human gaze shifting path by generating a number of superpixels in each image we construct graphlets to describe different objects they function as the object shaped receptive fields for image comparison due to the huge number of graphlets in an image a saliency guided graphlet selection algorithm is proposed a manifold embedding algorithm encodes graphlets with the semantics of training image tags then we derive a manifold propagation to calculate the postembedding graphlets by leveraging visual saliency maps the sequentially propagated graphlets constitute a path that mimics human gaze shifting finally we use the learned graphlet path as receptive fields for local image descriptor pooling the local descriptors from similar receptive fields of pairwise images more significantly contribute to the final image kernel thorough experiments demonstrate the advantage of our approach

indexing hierarchical structures using graph spectra hierarchical image structures are abundant in computer vision and have been used to encode part structure scale spaces and a variety of multiresolution features in this paper we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic graph dag into a low dimensional vector space based on a novel spectral characterization of a dag this topological signature allows us to efficiently retrieve a promising set of candidates from a database of models using a simple nearest neighbor search we establish the insensitivity of the signature to minor perturbation of graph structure due to noise occlusion or node split merge to accommodate large scale occlusion the dag rooted at each nonleaf node of the query votes for model objects that share that part effectively accumulating local evidence in a model dag s topological subspaces we demonstrate the approach with a series of indexing experiments in the domain of view based 3d object recognition using shock graphs

matching hierarchical structures using association graphs it is well known that the problem of matching two relational structures can be posed as an equivalent problem of finding a maximal clique in a derived association graph however it is not clear how to apply this approach to computer vision problems where the graphs are hierarchically organized i e are trees since maximal cliques are not constrained to preserve the partial order we provide a solution to the problem of matching two trees by constructing the association graph using the graph theoretic concept of connectivity we prove that in the new formulation there is a one to one correspondence between maximal cliques and maximal subtree isomorphisms this allows us to cast the tree matching problem as an indefinite quadratic program using the motzkin straus theorem and we use replicator dynamical systems developed in theoretical biology to solve it such continuous solutions to discrete problems are attractive because they can motivate analog and biological implementations the framework is also extended to the matching of attributed trees by using weighted association graphs we illustrate the power of the approach by matching articulated and deformed shapes described by shock trees

damping of beam vibrations by means of a thin constrained viscoelastic layer evaluation of a new theory abstract in this paper a new one dimensional theory of beams damped by means of a thin viscoelastic film is presented based upon a prescribed piecewise linear longitudinal displacement field and a shear stress field satisfying continuity conditions at the interfaces the beam formulation of the proposed theory is derived in order to demonstrate the accuracy of this beam model numerical tests were carried out by using the finite element method the present results are compared with well known analytical finite element with the msc nastran computer program and experimental solutions under various material temperature and boundary conditions it is found that the proposed theory is very simple and efficient in predicting the dynamic response of beams that are damped by means of a thin viscoelastic film

robust symbolic representation for shape recognition and retrieval a new method for shape recognition and retrieval is proposed here the suggested algorithm is based on several steps the algorithm analyzes the contour of pairs of shapes their contours are recovered and represented by a pair of n points obtained by linear interpolation given two points p i and q j from the two shapes the cost of their matching is evaluated by using the shape context and by using dynamic programming the best matching between the point sets is obtained dynamic programming not only recovers the best matching but also identifies occlusions i e points in the two shapes which cannot be properly matched given the correspondence between the two point sets the two contours are aligned using procrustes analysis after alignment each contour is transformed into a string of symbols and a modified version of edit distance is used to compute the similarity between strings of symbols finally recognition and retrieval are obtained by a simple nearest neighbor procedure the algorithm has been tested on a large set of shape databases kimia mpeg 7 natural silhouette database gesture database marine database swedish leaf database diatom database eth 80 3d object database providing performances for both in recognition and in retrieval superior to most of previously proposed approaches

morphometric tools for landmark data geometry and biology 1 introduction 2 preliminaries 3 landmarks 4 distance measures 5 shape coordinates 6 principal axes of shape change for triangles 7 features of shape comparison 8 retrospect and prospect

testing metric properties finite metric spaces and in particular tree metrics play an important role in various disciplines such as evolutionary biology and statistics a natural family of problems concerning metrics is deciding given a matrix m whether or not it is a distance metric of a certain predetermined type here we consider the following relaxed version of such decision problems for any given matrix m and parameter e we are interested in determining by probing m whether m has a particular metric property p or whether it is e far from having the property in e far we mean that at least an e fraction of the entries of m must be modified so that it obtains the property the algorithm may query the matrix on entries m i j of its choice and is allowed a constant probability of error we describe algorithms for testing euclidean metrics tree metrics and ultrametrics furthermore we present an algorithm that tests whether a matrix m is an approximate ultrametric in all cases the query complexity and running time are polynomial in 1 e and independent of the size of the matrix finally our algorithms can be used to solve relaxed versions of the corresponding search problems in time that is sub linear in the size of the matrix

reversed processes product forms and a non product form the equilibrium joint state probabilities of interacting markov processes are obtained in a hierarchical way by finding the reversed process of the interaction in terms of the reversed processes of its components from a reversed process a product form solution for the joint state probabilities follows directly the method uses a markovian process algebra formalism and generalises the recent reversed compound agent theorem rcat to solve a diverse class of concurrent systems this class includes processes with shared exclusive resources a customer oriented specification of a last come first served lcfs queue with coxian service times and an extended ps queue with a non product form solution from these results a new very short proof of the bcmp theorem ensues the principal advantage of the methodology is its potential for mechanisation and symbolic implementation indeed many non standard product forms have emerged directly from the compositional approach

an optimal control approach to probabilistic boolean networks abstract external control of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases for this purpose a number of stochastic optimal control approaches have been proposed probabilistic boolean networks pbns as powerful tools for modeling gene regulatory systems have attracted considerable attention in systems biology in this paper we deal with a problem of optimal intervention in a pbn with the help of the theory of discrete time markov decision process specifically we first formulate a control model for a pbn as a first passage model for discrete time markov decision processes and then find using a value iteration algorithm optimal effective treatments with the minimal expected first passage time over the space of all possible treatments in order to demonstrate the feasibility of our approach an example is also displayed

steady state solution of probabilistic gene regulatory networks we introduce a probability model for gene regulatory networks based on a system of chapman kolmogorov equations that represent the dynamics of the concentration levels of each agent in the network this unifying approach includes the representation of excitatory and inhibitory interactions between agents second order interactions which allow any two agents to jointly act on other agents and boolean dependencies between agents the probability model represents the concentration or quantity of each agent and we obtain the equilibrium solution for the joint probability distribution of each of the concentrations the result is an exact solution in product form where the joint equilibrium probability distribution of the concentration for each gene is the product of the marginal distribution for each of the concentrations the analysis we present yields the probability distribution of the concentration or quantity of all of the agents in a network that includes both logical dependencies and excitatory inhibitory relationships between agents

discovering discriminative graphlets for aerial image categories recognition recognizing aerial image categories is useful for scene annotation and surveillance local features have been demonstrated to be robust to image transformations including occlusions and clutters however the geometric property of an aerial image i e the topology and relative displacement of local features which is key to discriminating aerial image categories cannot be effectively represented by state of the art generic visual descriptors to solve this problem we propose a recognition model that mines graphlets from aerial images where graphlets are small connected subgraphs reflecting both the geometric property and color texture distribution of an aerial image more specifically each aerial image is decomposed into a set of basic components e g road and playground and a region adjacency graph rag is accordingly constructed to model their spatial interactions aerial image categories recognition can subsequently be casted as rag to rag matching based on graph theory rag to rag matching is conducted by comparing all their respective graphlets because the number of graphlets is huge we derive a manifold embedding algorithm to measure different sized graphlets after which we select graphlets that have highly discriminative and low redundancy topologies through quantizing the selected graphlets from each aerial image into a feature vector we use support vector machine to discriminate aerial image categories experimental results indicate that our method outperforms several state of the art object scene recognition models and the visualized graphlets indicate that the discriminative patterns are discovered by our proposed approach

provably fast and accurate recovery of evolutionary trees through harmonic greedy triplets we give a greedy learning algorithm for reconstructing an evolutionary tree based on a certain harmonic average on triplets of terminal taxa after the pairwise distances between terminal taxa are estimated from sequence data the algorithm runs in smallbigo numtaxa 2 time using smallbigo numtaxa work space where numtaxa is the number of terminal taxa these time and space complexities are optimal in the sense that the size of an input distance matrix is numtaxa 2 and the size of an output tree is numtaxa moreover in the jukes cantor model of evolution the algorithm recovers the correct tree topology with high probability using sample sequences of length polynomial in 1 numtaxa 2 the logarithm of the error probability and 3 the inverses of two small parameters

view based recognition using an eigenspace approximation to the hausdorff measure view based recognition methods such as those using eigenspace techniques have been successful for a number of recognition tasks such approaches however are somewhat limited in their ability to recognize objects that are partly hidden from view or occur against cluttered backgrounds in order to address these limitations we have developed a view matching technique based on an eigenspace approximation to the generalized hausdorff measure this method achieves compact storage and fast indexing that are the main advantages of eigenspace view matching techniques while also being tolerant of partial occlusion and background clutter the method applies to binary feature maps such as intensity edges rather than directly to intensity images

generalized median graph computation by means of graph embedding in vector spaces the median graph has been presented as a useful tool to represent a set of graphs nevertheless its computation is very complex and the existing algorithms are restricted to use limited amount of data in this paper we propose a new approach for the computation of the median graph based on graph embedding graphs are embedded into a vector space and the median is computed in the vector domain we have designed a procedure based on the weighted mean of a pair of graphs to go from the vector domain back to the graph domain in order to obtain a final approximation of the median graph experiments on three different databases containing large graphs show that we succeed to compute good approximations of the median graph we have also applied the median graph to perform some basic classification tasks achieving reasonable good results these experiments on real data open the door to the application of the median graph to a number of more complex machine learning algorithms where a representative of a set of graphs is needed

flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes curve matching is one instance of the fundamental correspondence problem our flexible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations a syntactic representation is constructed for both curves and an edit transformation which maps one curve to the other is found using dynamic programming we present extensive experiments where we apply the algorithm to silhouette matching in these experiments we examine partial occlusion viewpoint variation articulation and class matching where silhouettes of similar objects are matched based on the qualitative syntactic matching we define a dissimilarity measure and we compute it for every pair of images in a database of 121 images we use this experiment to objectively evaluate our algorithm first we compare our results to those reported by others second we use the dissimilarity values in order to organize the image database into shape categories the veridical hierarchical organization stands as evidence to the quality of our matching and similarity estimation

improving skeletal shape abstraction using multiple optimal solutions shape abstraction is an important problem faced by researchers in many fields such as pattern recognition computer vision and industrial design a recently developed previous shape abstraction framework demirci et al 20 generates an abstracted shape based on the correspondences between the features of the input shapes where the correspondences are obtained using the first optimal solution of a well known transportation problem as the size of the feature space grows the possibility of having more than one optimal solution for the same problem increases considering the case where multiple optimal solutions exist for the same transportation problem we first rank all optimal solutions based on how much they preserve the local neighborhood relations in this paper instead of creating the abstracted shape using the first optimal solution as done by the previous work we create the abstracted shape using the highest ranked optimal solution with this new property more effective abstracted shapes are generated experimental evaluation of the framework demonstrates that the proposed approach compares favorably with the previous technique in a set of shape retrieval experiments for different datasets highlightsskeletal shape abstraction is reformulated as the transportation problem a distortion free graph embedding is used for a better representation all optimal solutions for the transportation problem are considered the optimal solution which most resembles the neighborhood relations is used experiments demonstrate the improved performance

correlated logistic model with elastic net regularization for multilabel image classification in this paper we present correlated logistic corrlog model for multilabel image classification corrlog extends conventional logistic regression model into multilabel cases via explicitly modeling the pairwise correlation between labels in addition we propose to learn the model parameters of corrlog with elastic net regularization which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification corrlog can be efficiently learned though approximately by regularized maximum pseudo likelihood estimation and it enjoys a satisfying generalization bound that is independent of the number of labels corrlog performs competitively for multilabel image classification on benchmark data sets mulan scene mit outdoor scene pascal voc 2007 and pascal voc 2012 compared with the state of the art multilabel classification algorithms

local grayvalue invariants for image retrieval this paper addresses the problem of retrieving images from large image databases the method is based on local grayvalue invariants which are computed at automatically detected interest points a voting algorithm and semilocal constraints make retrieval possible indexing allows for efficient retrieval from a database of more than 1 000 images experimental results show correct retrieval in the case of partial visibility similarity transformations extraneous features and small perspective deformations

unified detection of skewed rotation reflection and translation symmetries from affine invariant contour features symmetry detection is significant for object detection and recognition since symmetries are salient cues for distinguishing geometrical structures from cluttered backgrounds this paper proposes a unified framework to detect rotation reflection and translation symmetries simultaneously on unsegmented real world images the detection is performed based on affine invariant contour features and is applicable under skewed imaging with distortions contours on a natural image are first matched to each other to find affine invariant matching pairs which are then classified into three groups using a sign change criterion corresponding to the three types of symmetries the three groups are used to vote for the corresponding symmetries the voting for rotation utilizes a scheme of short line segments the voting for reflection is based on a parameterization of axis equation and the voting for translation uses a cascade like approach experimental results of real world images are provided with quantitative evaluations validating that the proposed framework achieves desired performance

rigid shape matching by segmentation averaging we use segmentations to match images by shape the new matching technique does not require point to point edge correspondence and is robust to small shape variations and spatial shifts to address the unreliability of segmentations computed bottom up we give a closed form approximation to an average over all segmentations our method has many extension yielding new algorithms for tracking object detection segmentation and edge preserving smoothing for segmentation instead of a maximum a posteriori approach we compute the central segmentation minimizing the average distance to all segmentations of an image for smoothing instead of smoothing images based on local structures we smooth based on the global optimal image structures our methods for segmentation smoothing and object detection perform competitively and we also show promising results in shape based tracking

bridging genetic networks and queueing theory one of the main challenges facing biology today is the understanding of the joint action of genes proteins and rna molecules interwoven in intricate interdependencies commonly known as genetic networks to this end several mathematical approaches have been introduced to date in addition to developing the analytical tools required for this task anew one can utilize knowledge found in existing disciplines specializing in the representation and analysis of systems featuring similar aspects we suggest queueing theory as a possible source of such knowledge this discipline which focuses on the study of workloads forming in a variety of scenarios offers an assortment of tools allowing for the derivation of the statistical properties of the inspected systems we argue that a proper adaptation of modeling techniques and analytical methods used in queueing theory can contribute to the study of genetic regulatory networks this is demonstrated by presenting a queueing inspired model of a genetic network of arbitrary size and structure for which the probability distribution function is derived this model is further applied to the description of the lac operon regulation mechanism in addition we discuss the possible benefits stemming for queueing theory from the interdisciplinary dialogue with molecular biology in particular the incorporation of various dynamical behaviours into queueing networks

unsupervised category modeling recognition and segmentation in images suppose a set of arbitrary unlabeled images contains frequent occurrences of 2d objects from an unknown category this paper is aimed at simultaneously solving the following related problems 1 unsupervised identification of photometric geometric and topological properties of multiscale regions comprising instances of the 2d category 2 learning a region based structural model of the category in terms of these properties and 3 detection recognition and segmentation of objects from the category in new images to this end each image is represented by a tree that captures a multiscale image segmentation the trees are matched to extract the maximally matching subtrees across the set which are taken as instances of the target category the extracted subtrees are then fused into a tree union that represents the canonical category model detection recognition and segmentation of objects from the learned category are achieved simultaneously by finding matches of the category model with the segmentation tree of a new image experimental validation on benchmark data sets demonstrates the robustness and high accuracy of the learned category models when only a few training examples are used for learning without any human supervision

graph matching by simplified convex concave relaxation procedure the convex and concave relaxation procedure ccrp was recently proposed and exhibited state of the art performance on the graph matching problem however ccrp involves explicitly both convex and concave relaxations which typically are difficult to find and thus greatly limit its practical applications in this paper we propose a simplified ccrp scheme which can be proved to realize exactly ccrp but with a much simpler formulation without needing the concave relaxation in an explicit way thus significantly simplifying the process of developing ccrp algorithms the simplified ccrp can be generally applied to any optimizations over the partial permutation matrix as long as the convex relaxation can be found based on two convex relaxations we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix respectively extensive experimental results witness the simplicity as well as state of the art performance of the two simplified ccrp graph matching algorithms

random neural networks with multiple classes of signals by extending the pulsed recurrent random neural network rnn discussed in gelenbe 1989 1990 1991 we propose a recurrent random neural network model in which each neuron processes several distinctly characterized streams of signals or data the idea that neurons may be able to distinguish between the pulses they receive and use them in a distinct manner is biologically plausible in engineering applications the need to process different streams of information simultaneously is commonplace e g in image processing sensor fusion or parallel processing systems in the model we propose each distinct stream is a class of signals in the form of spikes signals may arrive to a neuron from either the outside world exogenous signals or other neurons endogenous signals as a function of the signals it has received a neuron can fire and then send signals of some class to another neuron or to the outside world we show that the multiple signal class random model with exponential interfiring times