elastically adaptive deformable models we present a technique for the automatic adaptation of a deformable model s elastic parameters within a kalman filter framework for shape estimation applications the novelty of the technique is that the model s elastic parameters are not constant but spatio temporally varying the variation of the elastic parameters depends on the distance of the model from the data and the rate of change of this distance each pass of the algorithm uses physics based modeling techniques to iteratively adjust both the geometric and the elastic degrees of freedom of the model in response to forces that are computed from the discrepancy between the model and the data by augmenting the state equations of an extended kalman filter to incorporate these additional variables we are able to significantly improve the quality of the shape estimation therefore the model s elastic parameters are always initialized to the same value and they are subsequently modified depending on the data and the noise distribution we present results demonstrating the effectiveness of our method for both two dimensional and three dimensional data

fixed parameter algorithms for kemeny rankings the computation of kemeny rankings is central to many applications in the context of rank aggregation given a set of permutations votes over a set of candidates one searches for a consensus permutation that is closest to the given set of permutations unfortunately the problem is np hard we provide a broad study of the parameterized complexity for computing optimal kemeny rankings besides the three obvious parameters number of votes number of candidates and solution size called kemeny score we consider further structural parameterizations more specifically we show that the kemeny score and a corresponding kemeny ranking of an election can be computed efficiently whenever the average pairwise distance between two input votes is not too large in other words kemeny score is fixed parameter tractable with respect to the parameter average pairwise kendall tau distance d a we describe a fixed parameter algorithm with running time 16 d a email protected poly moreover we extend our studies to the parameters maximum range and average range of positions a candidate takes in the input votes whereas kemeny score remains fixed parameter tractable with respect to the parameter maximum range it becomes np complete in the case of an average range of two this excludes fixed parameter tractability with respect to the parameter average range unless p np finally we extend some of our results to votes with ties and incomplete votes where in both cases one no longer has permutations as input

damped lateral vibrations of straight and curved cables with no axial pre load abstract this paper presents a study of the lateral vibrations of straight and curved cables with no axial pre load for the computation of the vibration transmissibility we used finite elements based on the euler bernoulli theory the dissipation of energy was studied with viscous and structural damping models where the rayleigh coefficients and the frequency dependence of the loss factor were identified by using the equality between the measured and the computed natural frequencies the frequency dependence of the dynamic modulus of elasticity was estimated and used for all the studied types of cable the excitation was the result of moving the support in a direction perpendicular to the axis of the cable the mathematical model for the computation of the vibrations of the straight and curved cables was verified with experimental measurements in which the support excitation was achieved with an electrodynamic shaker and the amplitude force was measured at the fixed support with a dynamometer for the curved cable the mathematical model was verified for in plane and out off plane vibrations three straight cables of different lengths were analyzed for the dependence of the rayleigh coefficients on the length of the cables

using extension sets to aggregate partial rankings in a flexible setting this paper deals with the rank aggregation problem in a general setting in particular we approach the problem for any kind of ranking complete or incomplete and with or without ties the underlying idea behind our approach is to take into account the so called extension set of a ranking that is the set of permutations that are compatible with the given ranking in this way we aim to manage the uncertainty inherent to this problem when not all the items are ranked and or when some items are equally preferred we develop two approaches a general one based on this idea and a constrained version in which the extension set is limited by not allowing non ranked items to be placed in an existent bucket of tied items we test our proposal by coupling it with two different algorithms we formalize our approaches mathematically and also carry out an extensive experimental evaluation by using 22 datasets the results show that the use of our extension sets based approaches to compute the precedence matrix clearly outperforms the standard way of computing the preference matrix by only using the information explicitly provided by the rankings in the sample

on box totally dual integral polyhedra edmonds and giles introduced the class of box totally dual integral polyhedra as a generalization of submodular flow polyhedra in this paper a geometric characterization of these polyhedra is given this geometric result is used to show that each tdi defining system for a box tdi polyhedron is in fact a box tdi system that the class of box tdi polyhedra is in co np and is closed under taking projections and dominants that the class of box perfect graphs is in co np and a result of edmonds and giles which is related to the facets of box tdi polyhdera

complete elastic characterization of viscoelastic materials by dynamic measurements of the complex bulk and young s moduli as a function of temperature and hydrostatic pressure abstract two independent systems to measure the dynamic complex young s and bulk moduli of viscoelastic materials as a function of temperature and hydrostatic pressure are described in the young s modulus system a bar shaped sample is adhered to a piezoelectric shaker and mounted vertically inside an air filled pressure vessel data are obtained using both the traditional resonant approach and a wave speed technique in the bulk modulus system the compressibility of a sample of arbitrary shape immersed in castor oil and placed inside a pressure chamber is measured data can be obtained at frequencies typically ranging from 50 hz to 5 khz at temperatures comprised between 2 and 50 c and under hydrostatic pressures ranging from 0 to 2 mpa young s or 6 5 mpa bulk typical data obtained with both systems are presented and it is shown how these data can be combined to completely characterize the elasticity of the material under investigation in particular they can be used to obtain experimental values of the complex poisson s ratio whose accurate measurement is otherwise quite challenging to perform directly as an example the magnitude and loss tangent of poisson s ratio are presented for a nearly incompressible rubber

when is the matching polytope box totally dual integral let g v e be a graph the matching polytope of g denoted by p g is the convex hull of the incidence vectors of all matchings in g as proved by edmonds 10 edmonds j 1965 maximum matching and a polyhedron with 0 1 vertices j res nat bur standards sect b 69 1 2 125 130 p g is determined by the following linear system g x e 0 for each e e x v 1 for each v v and x e u u for each u v with u odd in 1978 cunningham and marsh 6 cunningham w marsh a 1978 a primal algorithm for optimum matching balinski ml hoffman aj eds polyhedral combinatorics mathematical programming studies vol 8 springer berlin 50 72 strengthened this theorem by showing that g is always totally dual integral in 1984 edmonds and giles 11 edmonds j giles r 1984 total dual integrality of linear inequality systems progress in combinatorial optimization academic press toronto 117 129 initiated the study of graphs g for which g is box totally dual integ

a min max theorem on tournaments we present a structural characterization of all tournaments t v a such that for any nonnegative integral weight function defined on v the maximum size of a feedback vertex set packing is equal to the minimum weight of a triangle in t we also answer a question of frank by showing that it is np complete to decide whether the vertex set of a given tournament can be partitioned into two feedback vertex sets in addition we give exact and approximation algorithms for the feedback vertex set packing problem on tournaments

a characterization of box mengerian matroid ports let m be a matroid on e l where l e is a distinguished element of m the l port of m is the set p p p e with p l a circuit of m let a be the p e incidence matrix let u2 4 be the uniform matroid on four elements of rank two let f7 be the fano matroid let f7 be the dual of f7 and let f7 be the unique series extension of f7 in this paper we prove that the system ax 1 x 0 is box totally dual integral box tdi if and only if m has no u2 4 minor using l no f7 minor using l and no f7 minor using l as a series element our characterization yields a number of interesting results in combinatorial optimization

statistical ranking and combinatorial hodge theory we propose a technique that we call hodgerank for ranking data that may be incomplete and imbalanced characteristics common in modern datasets coming from e commerce and internet applications we are primarily interested in cardinal data based on scores or ratings though our methods also give specific insights on ordinal data from raw ranking data we construct pairwise rankings represented as edge flows on an appropriate graph our statistical ranking method exploits the graph helmholtzian which is the graph theoretic analogue of the helmholtz operator or vector laplacian in much the same way the graph laplacian is an analogue of the laplace operator or scalar laplacian we shall study the graph helmholtzian using combinatorial hodge theory which provides a way to unravel ranking information from edge flows in particular we show that every edge flow representing pairwise ranking can be resolved into two orthogonal components a gradient flow that represents the l 2 optimal global ranking and a divergence free flow cyclic that measures the validity of the global ranking obtained if this is large then it indicates that the data does not have a good global ranking this divergence free flow can be further decomposed orthogonally into a curl flow locally cyclic and a harmonic flow locally acyclic but globally cyclic these provides information on whether inconsistency in the ranking data arises locally or globally when applied to statistical ranking problems hodge decomposition sheds light on whether a given dataset may be globally ranked in a meaningful way or if the data is inherently inconsistent and thus could not have any reasonable global ranking in the latter case it provides information on the nature of the inconsistencies an obvious advantage over the np hardness of kemeny optimization is that hodgerank may be easily computed via a linear least squares regression we also discuss connections with well known ordinal ranking techniques such as kemeny optimization and borda count from social choice theory

the poisson s loss factor of solid viscoelastic materials the complex poisson s ratio plays an important role in characterizing the linear dynamic behaviour of solid materials and occurs in a number of equations used for acoustical and vibration calculus the ratio of the imaginary part to the real part of complex poisson s ratio is referred to as poisson s loss factor the magnitude of the poisson s loss factor is investigated in this paper for homogeneous isotropic linear solid viscoelastic materials with positive poisson s ratio the relation of the poisson s loss factor to the material damping is determined it is shown that the magnitude of the poisson s loss factor is approximately proportional to the difference between the shear and bulk loss factors and is a rational fractional function of the dynamic poisson s ratio in addition relationships are developed which enable one to determine the approximate magnitude of the poisson s loss factor from knowledge only of the shear loss factor and the dynamic poisson s ratio it is shown that the poisson s loss factor is smaller than the shear loss factor usually by one order of magnitude at least moreover it is pointed out that the poisson s loss factor of a high loss and a low loss material may be about the same experimental data on two rubbers and a hard plastic are presented to verify the theoretical conclusions

a method for registration of 3 d shapes the authors describe a general purpose representation independent method for the accurate and computationally efficient registration of 3 d shapes including free form curves and surfaces the method handles the full six degrees of freedom and is based on the iterative closest point icp algorithm which requires only a procedure to find the closest point on a geometric entity to a given point the icp algorithm always converges monotonically to the nearest local minimum of a mean square distance metric and the rate of convergence is rapid during the first few iterations therefore given an adequate set of initial rotations and translations for a particular class of objects with a certain level of shape complexity one can globally minimize the mean square distance metric over all six degrees of freedom by testing each initial registration one important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection experimental results show the capabilities of the registration algorithm on point sets curves and surfaces

shape from silhouette across time part i theory and algorithms shape from silhouette sfs is a shape reconstruction method which constructs a 3d shape estimate of an object using silhouette images of the object the output of a sfs algorithm is known as the visual hull vh traditionally sfs is either performed on static objects or separately at each time instant in the case of videos of moving objects in this paper we develop a theory of performing sfs across time estimating the shape of a dynamic object with unknown motion by combining all of the silhouette images of the object over time we first introduce a one dimensional element called a bounding edge to represent the visual hull we then show that aligning two visual hulls using just their silhouettes is in general ambiguous and derive the geometric constraints in terms of bounding edges that govern the alignment to break the alignment ambiguity we combine stereo information with silhouette information and derive a temporal sfs algorithm which consists of two steps 1 estimate the motion of the objects over time visual hull alignment and 2 combine the silhouette information using the estimated motion visual hull refinement the algorithm is first developed for rigid objects and then extended to articulated objects in the part ii of this paper we apply our temporal sfs algorithm to two human related applications 1 the acquisition of detailed human kinematic models and 2 marker less motion tracking

on total dual integrality abstract we prove that each rational polyhedron of full dimension is determined by a unique minimal total dual integral system of linear inequalities with integral left hand sides thus extending a result of giles and pulleyblank and we give a characterization of total dual integrality

box total dual integrality box integrality and equimodular matrices box totally dual integral box tdi polyhedra are polyhedra described by systems which yield strong min max relations we characterize them in several ways involving the notions of principal box integer polyhedra and equimodular matrices a polyhedron is box integer if its intersection with any integer box ell le x le u is integer we define principally box integer polyhedra to be the polyhedra p such that kp is box integer whenever kp is integer a rational r times n matrix is equimodular if it has full row rank and its nonzero r times r determinants all have the same absolute value a face defining matrix is a full row rank matrix describing the affine hull of a face of the polyhedron our main result is that the following statements are equivalent along our proof we show that a polyhedral cone is box tdi if and only if it is box integer and that these properties are carried over to its polar we illustrate these charaterizations by reviewing well known results about box tdi polyhedra we also provide several applications the first one is a new perspective on the equivalence between two results about binary clutters secondly we refute a conjecture of ding zang and zhao about box perfect graphs thirdly we discuss connections with an abstract class of polyhedra having the integer caratheodory property finally we characterize the box tdiness of the cone of conservative functions of a graph and provide a corresponding box tdi system

hybrid sampling based clustering ensemble with global and local constitutions among a number of ensemble learning techniques boosting and bagging are the most popular sampling based ensemble approaches for classification problems boosting is considered stronger than bagging on noise free data set with complex class structures whereas bagging is more robust than boosting in cases where noise data are present in this paper we extend both ensemble approaches to clustering tasks and propose a novel hybrid sampling based clustering ensemble by combining the strengths of boosting and bagging in our approach the input partitions are iteratively generated via a hybrid process inspired by both boosting and bagging then a novel consensus function is proposed to encode the local and global cluster structure of input partitions into a single representation and applies a single clustering algorithm to such representation to obtain the consolidated consensus partition our approach has been evaluated on 2 d synthetic data collection of benchmarks and real world facial recognition data sets which show that the proposed technique outperforms the existing benchmarks for a variety of clustering tasks

deterministic pivoting algorithms for constrained ranking and clustering problems we consider ranking and clustering problems related to the aggregation of inconsistent information in particular rank aggregation weighted feedback arc set in tournaments consensus and correlation clustering and hierarchical clustering ailon et al ailon n m charikar a newman 2005 aggregating inconsistent information ranking and clustering proc 37th annual acm sympos theory comput stoc 05 684 693 ailon and charikar ailon n m charikar 2005 fitting tree metrics hierarchical clustering and phylogeny proc 46th annual ieee sympos foundations comput sci focs 05 73 82 and ailon ailon n 2007 aggregation of partial rankings p ratings and top m lists proc 18th annual acm siam sympos discrete algorithms soda 07 415 424 proposed randomized constant factor approximation algorithms for these problems which recursively generate a solution by choosing a random vertex as pivot and dividing the remaining vertices into two groups based on the pivot vertex in this paper we answer an open question in these works by giving deterministic approximation algorithms for these problems the analysis of our algorithms is simpler than the analysis of the randomized algorithms in addition we consider the problem of finding minimum cost rankings and clusterings that must obey certain constraints e g an input partial order in the case of ranking problems which were introduced by hegde and jain hegde r k jain 2006 personal communication we show that the first type of algorithms we propose can also handle these constrained problems in addition we show that in the case of a rank aggregation or consensus clustering problem if the input rankings or clusterings obey the constraints then we can always ensure that the output of any algorithm obeys the constraints without increasing the objective value of the solution

suppression of time aliasing in the solution of the equations of motion of an impacted beam with partial constrained layer damping abstract signal processing issues encountered when an analytical spectrum is converted in the time domain with an inverse discrete fourier transform are investigated in the analytical model of the transient time response of an impacted beam with partial constrained layer damping pcld developed in ref d granger a ross effects of partial constrained viscoelastic layer damping parameters on the initial transient response of impacted cantilever beams experimental and numerical results journal of sound and vibration 321 1 2 2009 45 64 doi 10 1016 j jsv 2008 09 039 noncausal effects were observed for lightly damped structures as discussed in the present paper the noncausal effects were due to time aliasing occurring when continuous frequency spectra were discretized to suppress such errors the numerical laplace transform is introduced and applied to the previous model which was based on fourier transforms the equations of motion of the system and the viscoelastic properties of the core are formulated in the laplace domain a window is used in the laplace domain to avoid amplification of the gibbs oscillations that are caused by the truncation of the spectrum the new solution technique is compared to the previous method it is shown that noncausal effects appearing in the first milliseconds of time signals with the use of discrete fourier transforms are avoided with the laplace transform solution method numerical results are validated for transient responses using experimental impact force signals the results are in good agreement with experimental data

a min max relation on packing feedback vertex sets let g be a graph with a nonnegative integral function w defined on v g a collection f of subsets of v g repetition is allowed is called a feedback vertex set packing in g if the removal of any member of f from g leaves a forest and every vertex v v g is contained in at most w v members of f the weight of a cycle c in g is the sum of w v over all vertices v of c the purpose of this paper is to characterize all graphs with the property that for any nonnegative integral function w the maximum cardinality of a feedback vertex set packing is equal to the minimum weight of a cycle

some optimal inapproximability results we prove optimal up to an arbitrary e 0 inapproximability results for max e k sat for k 3 maximizing the number of satisfied linear equations in an over determined system of linear equations modulo a prime p and set splitting as a consequence of these results we get improved lower bounds for the efficient approximability of many optimization problems studied previously in particular for max e2 sat max cut max di cut and vertex cover

predicting link directions via a recursive subgraph based ranking abstract link directions are essential to the functionality of networks and their prediction is helpful toward a better knowledge of directed networks from incomplete real world data we study the problem of predicting the directions of some links by using the existence and directions of the rest of links we propose a solution by first ranking nodes in a specific order and then predicting each link as stemming from a lower ranked node and pointing toward a higher ranked one the proposed ranking method works recursively by utilizing local indicators on multiple scales each corresponding to a subgraph extracted from the original network experiments on real networks show that the directions of a substantial fraction of links can be correctly recovered by our method which outperforms either purely local or global methods

aggregating inconsistent information ranking and clustering we address optimization problems in which we are given contradictory pieces of input information and the goal is to find a globally consistent solution that minimizes the extent of disagreement with the respective inputs specifically the problems we address are rank aggregation the feedback arc set problem on tournaments and correlation and consensus clustering we show that for all these problems and various weighted versions of them we can obtain improved approximation factors using essentially the same remarkably simple algorithm additionally we almost settle a long standing conjecture of bang jensen and thomassen and show that unless np bpp there is no polynomial time algorithm for the problem of minimum feedback arc set in tournaments

evaluating combinational illumination estimation methods on real world images illumination estimation is an important component of color constancy and automatic white balancing a number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature these combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions the existing combinational methods are surveyed and analyzed here with the goals of determining 1 the effectiveness of fusing illumination estimates from multiple subordinate methods 2 the best method of combination 3 the underlying factors that affect the performance of a combinational method and 4 the effectiveness of combination for illumination estimation in multiple illuminant scenes the various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high level scene content cues e g indoor versus outdoor extensive tests and enhanced analyzes using three data sets of real world images are conducted for consistency in testing the images were labeled according to their high level features 3d stages indoor outdoor and this label data is made available on line the tests reveal that the trained combinational methods direct combination by support vector regression in particular clearly outperform both the non combinational methods and those combinational methods based on scene content cues

finding good 2 partitions of digraphs i hereditary properties we study the complexity of deciding whether a given digraph d has a vertex partition into two disjoint subdigraphs with given structural properties let h and e denote the following two sets of natural properties of digraphs h acyclic complete arcless oriented no 2 cycle semicomplete symmetric tournament and e strongly connected connected minimum out degree at least 1 minimum in degree at least 1 minimum semi degree at least 1 minimum degree at least 1 having an out branching having an in branching in this paper we determine the complexity of deciding for any fixed pair of positive integers k 1 k 2 whether a given digraph has a vertex partition into two digraphs d 1 d 2 such that v d i k i and d i has property p i for i 1 2 when p 1 h and p 2 h e we also classify the complexity of the same problems when restricted to strongly connected digraphs the complexity of the 2 partition problems where both p 1 and p 2 are in e is determined in the companion paper 2

improved approximation algorithms for hitting 3 vertex paths we study the problem of deleting a minimum cost set of vertices from a given vertex weighted graph in such a way that the resulting graph has no induced path on three vertices this problem is often called cluster vertex deletion in the literature and admits a straightforward 3 approximation algorithm since it is a special case of the vertex cover problem on a 3 uniform hypergraph recently you wang and cao described an efficient 5 2 approximation algorithm for the unweighted version of the problem our main result is a 9 4 approximation algorithm for arbitrary weights using the local ratio technique we further conjecture that the problem admits a 2 approximation algorithm and give some support for the conjecture this is in sharp contrast with the fact that the similar problem of deleting vertices to eliminate all triangles in a graph is known to be ugc hard to approximate to within a ratio better than 3 as proved by guruswami and lee

computation of eigenvalues in proportionally damped viscoelastic structures based on the fixed point iteration abstract linear viscoelastic structures are characterized by dissipative forces that depend on the history of the velocity response via hereditary damping functions the free motion equation leads to a nonlinear eigenvalue problem characterized by a frequency dependent damping matrix in the present paper a novel and efficient numerical method for the computation of the eigenvalues of linear and proportional or lightly non proportional viscoelastic structures is developed the central idea is the construction of two complex valued functions of a complex variable whose fixed points are precisely the eigenvalues this important property allows the use of these functions in a fixed point iterative scheme with help of some results in fixed point theory necessary conditions for global and local convergence are provided it is demonstrated that the speed of convergence is linear and directly depends on the level of induced damping in addition under certain conditions the recursive method can also be used for the calculation of non viscous real eigenvalues in order to validate the mathematical results two numerical examples are analyzed one for single degree of freedom systems and another for multiple ones

a randomized ptas for the minimum consensus clustering with a fixed number of clusters the consensus clustering problem has been introduced as an effective way to analyze the results of different microarray experiments filkov and skiena 2004a b 1 2 the problem asks for a partition that summarizes a set of input partitions each corresponding to a different microarray experiment under a simple and intuitive cost the problem on instances with two input partitions has a simple polynomial time algorithm but it becomes apx hard on instances with three input partitions the quest for defining the boundary between tractable and intractable instances leads to the investigation of the restriction of consensus clustering when the output partition contains a fixed number of sets in this paper we give a randomized polynomial time approximation scheme for such problems while proving its np hardness even for 2 output partitions therefore definitively settling the approximation complexity of the problem

the bulk modulus and poisson s ratio of incompressible materials abstract poisson s ratio is a fundamental parameter characterizing the mechanical behavior of a material because the ratio of the bulk to the shear modulus b g becomes infinite when 1 2 it is often assumed that the bulk modulus becomes very large as a material approaches incompressibility this is incorrect experimental results for viscoelastic materials show that changes in the bulk modulus are actually negligible as approaches 1 2 an analysis is performed to clarify the apparent conflict between the classic elastic equations and the experiments at 1 2 the bulk modulus is shown to exhibit a singularity but this is irrelevant to real materials

three dimensional human body model acquisition from multiple views we present a novel approach to the three dimensional human body model acquisition from three mutually orthogonal views our technique is based on the spatiotemporal analysis of the deforming apparent contour of a human moving according to a protocol of movements for generality and robustness our technique does not use a prior model of the human body and a prior body part segmentation is not assumed therefore our technique applies to humans of any anthropometric dimension to parameterize and segment over time a deforming apparent contour we introduce a new shape representation technique based on primitive composition the composed deformable model allows us to represent large local deformations and their evolution in a compact and intuitive way in addition this representation allows us to hypothesize an underlying part structure and test this hypothesis against the relative motion due to forces exerted from the image data of the defining primitives of the composed model furthermore we develop a human body part decomposition algorithm hbpda that recovers all the body parts of a subject by monitoring the changes over time to the shape of the deforming silhouette in addition we modularize the process of simultaneous two dimensional part determination and shape estimation by employing the supervisory control theory of discrete event systems finally we present a novel algorithm which selectively integrates the segmented by the hbpda apparent contours from three mutually orthogonal viewpoints to obtain a three dimensional model of the subject s body parts the effectiveness of the approach is demonstrated through a series of experiments where a subject performs a set of movements according to a protocol that reveals the structure of the human body

clustering with local restrictions we study a family of graph clustering problems where each cluster has to satisfy a certain local requirement formally let m be a function on the subsets of vertices of a graph g in the m p q partition problem the task is to find a partition of the vertices into clusters where each cluster c satisfies the requirements that 1 at most q edges leave c and 2 m c

a generalized complex eigenvector method for dynamic analysis of heterogeneous viscoelastic structures a generalized complex eigenvector method which can be used to a linear dynamic analysis of viscoelastic structures is described here dynamic analysis is understood as transient analysis and frequency response analysis the generalized complex eigenvector method is based on nite element discretization of structure approximation of viscoelastic properties by di erential operators and mode superposition technique coe cients of di erential operator are de ned from the condition of best coincidence of complex characteristic of viscoelastic material and complex characteristic of di erential operator in preset frequency range advantage of this method is that it allows to take into account the real changes of the viscoelastic property in frequency range also the generalized complex eigenvector method permit to describe a viscoelastic properties by two functions complex young s modulus complex poisson s ratio the method is veri ed with the help of comparing with solutions obtained by complex modulus method an in uence of viscoelastic poisson s ratio on transient and frequency responses of structure is demonstrated copyright 2001 john wiley sons ltd

the kendall and mallows kernels for permutations we show that the widely used kendall tau correlation coefficient and the related mallows kernel are positive definite kernels for permutations they offer computationally attractive alternatives to more complex kernels on the symmetric group to learn from rankings or learn to rank we show how to extend these kernels to partial rankings multivariate rankings and uncertain rankings examples are presented on how to formulate typical problems of learning from rankings such that they can be solved with state of the art kernel algorithms we demonstrate promising results on clustering heterogeneous rank data and high dimensional classification problems in biomedical applications

on the approximability of dodgson and young elections the voting rules proposed by dodgson and young are both designed to find an alternative closest to being a condorcet winner according to two different notions of proximity the score of a given alternative is known to be hard to compute under either rule in this paper we put forward two algorithms for approximating the dodgson score a combinatorial greedy algorithm and an lp based algorithm both of which yield an approximation ratio of h m 1 where m is the number of alternatives and h m 1 is the m 1 st harmonic number we also prove that our algorithms are optimal within a factor of 2 unless problems in np have quasi polynomial time algorithms despite the intuitive appeal of the greedy algorithm we argue that the lp based algorithm has an advantage from a social choice point of view further we demonstrate that computing any reasonable approximation of the ranking produced by dodgson s rule is np hard this result provides a complexity theoretic explanation of sharp discrepancies that have been observed in the social choice theory literature when comparing dodgson elections with simpler voting rules finally we show that the problem of calculating the young score is np hard to approximate by any factor this leads to an inapproximability result for the young ranking

an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity given a set v of n elements we wish to linearly order them given pairwise preference labels which may be non transitive due to irrationality or arbitrary noise the goal is to linearly order the elements while disagreeing with as few pairwise preference labels as possible our performance is measured by two parameters the number of disagreements loss and the query complexity number of pairwise preference labels our algorithm adaptively queries at most o e 6n log5 n preference labels for a regret of e times the optimal loss as a function of n this is asymptotically better than standard non adaptive learning bounds achievable for the same problem our main result takes us a step closer toward settling an open problem posed by learning to rank from pairwise information theoreticians and practitioners what is a provably correct way to sample preference labels to further show the power and practicality of our solution we analyze a typical test case in which a large margin linear relaxation is used for efficiently solving the simpler learning problems in our decomposition

a primal dual approximation algorithm for the vertex cover p3 problem we introduce the vertex coverp n vcp n problem that is the problem of finding a minimum weight set email protected v such that the graph g v f has no p n where p n is a path with n vertices the problem also has its application background in this paper we first show that the vcp n problem is np hard for any integer n 2 then we restrict our attention to the vcp 3 problem and give a 2 approximation algorithm using the primal dual method

packing and covering with linear programming a survey this paper considers the polyhedral results and the min max results on packing and covering problems of the decade since the strong perfect graph theorem published in 2006 the main such results are available for the packing problem however there are still important polyhedral questions that remain open for the covering problem the main questions are still open although there has been important progress we survey some of the main results with emphasis on those where linear programming and graph theory come together they mainly concern the covering of cycles or dicycles in graphs or signed graphs either with vertices or edges this includes the multicut and integral multiflow problems

one third integrality in the max cut problem given a graphg v e the metric polytopes g is defined by the inequalitiesx f x c f f 1 for f subseteq c f odd c cycle ofg and 0 xe 1 fore e optimization overs g provides an approximation for the max cut problem the graphg is called 1 d integral if all the vertices ofs g have their coordinates in i d 0 i d we prove that the class of 1 d integral graphs is closed under minors and we present several minimal forbidden minors for 1 3 integrality in particular we characterize the 1 3 integral graphs on seven nodes we study several operations preserving 1 d integrality in particular thek sum operation for 0 k 3 we prove that series parallel graphs are characterized by the following stronger property all vertices of the polytopes g x l x u are 1 3 integral for every choice of 1 3 integral boundsl u on the edges ofg

the complexity of recognizing linear systems with certain integrality properties let a be a 0 1 matrix with precisely two 1 s in each column and let 1 be the all one vector we show that the problems of deciding whether the linear system a bf x ge bf 1 bf x ge bf 0 1 defines an integral polyhedron 2 is totally dual integral tdi and 3 box totally dual integral box tdi are all co np complete thereby confirming the conjecture on np hardness of recognizing tdi systems made by edmonds and giles in 1984

effects of partial constrained viscoelastic layer damping parameters on the initial transient response of impacted cantilever beams experimental and numerical results the effects of partial constrained viscoelastic layer damping on the first milliseconds of the transient vibration of an impacted beam is studied using an analytical model the viscoelastic properties of the core are frequency dependent and the shear modulus is modelled using a prony series the equations of motion of the system are obtained using lagrange s equations the equations of motion are converted in the frequency domain using a fourier transform and they are solved for frequency displacements using the assumed modes method they are then converted back in the time domain using an inverse fourier transform the technique is validated for transient responses using experimental impact force signals the numerical results are in good agreement with experimental data four partial constrained viscoelastic layer damping parameters are studied the length the placement the viscoelastic layer thickness and the constraining layer thickness it turns out that the length of the partial constrained viscoelastic layer damping has the most important effect on the initial transient displacement while the viscoelastic layer thickness has little effect noncausal effects in the model are discussed and are mainly induced when the partial constrained viscoelastic layer damping treatment is poorly effective

simulation based estimation of dynamic mechanical properties for viscoelastic materials used for vocal fold models abstract in order to obtain a deeper understanding of the human phonation process and the mechanisms generating sound realistic setups are built up containing artificial vocal folds usually these vocal folds consist of viscoelastic materials e g polyurethane mixtures reliable simulation based studies on the setups require the mechanical properties of the utilized viscoelastic materials the aim of this work is the identification of mechanical material parameters young s modulus poisson s ratio and loss factor for those materials therefore we suggest a low cost measurement setup the so called vibration transmission analyzer vta enabling to analyze the transfer behavior of viscoelastic materials for propagating mechanical waves with the aid of a mathematical inverse method the material parameters are adjusted in a convenient way so that the simulation results coincide with the measurement results for the transfer behavior contrary to other works we determine frequency dependent functions for the mechanical properties characterizing the viscoelastic material in the frequency range of human speech 100 250 hz the results for three different materials clearly show that the poisson s ratio is close to 0 5 and that the young s modulus increases with higher frequencies for a frequency of 400 hz the young s modulus of the investigated viscoelastic materials is approximately 80 higher than for the static case 0 hz we verify the identified mechanical properties with experiments on fabricated vocal fold models thereby only small deviations between measurements and simulations occur

a unified approach to box mengerian hypergraphs a hypergraph is called box mengerian if the linear system ax 1 x 0 is box totally dual integral box tdi where a is the edge vertex incidence matrix of the hypergraph because it is np hard in general to recognize box mengerian hypergraphs a basic theme in combinatorial optimization is to identify such objects associated with various problems in this paper we show that the so called equitably subpartitionable esp property first introduced by ding and zang ding g w zang 2002 packing cycles in graphs j combin theory ser b 86 381 407 in their characterization of all graphs with the min max relation on packing and covering cycles turns out to be even sufficient for box mengerian hypergraphs we also establish several new classes of box mengerian hypergraphs based on esp property this approach is of transparent combinatorial nature and is hence fairly easy to work with

on the hardness of labeled correlation clustering problem motivated by practical applications the labeled correlation clustering problem a variant of correlation clustering problem is formally defined and studied in this paper since the problem is np complete we consider the parameterized complexities three different parameterizations are considered and the corresponding parameterized complexities are studied for the two parameterized problems which are fixed parameter tractable the lower bounds of them are analyzed under seth strong exponential time hypothesis

verification of local kramers kronig relations for complex modulus by means of fractional derivative model abstract the local kramers kronig k k relations which link the damping properties of solid materials at one frequency to the rate of frequency variation of dynamic modulus are not exact the validity and accuracy of the local k k relations is theoretically investigated in this paper by means of material models especially the fractional zener model it is shown that the local k k relations qualitatively always properly predict the relation between the damping and the frequency dependence of dynamic modulus for any type of deformation and any linear mechanism of energy loss determining the frequency variations nevertheless the accuracy depends on the rate of frequency variation of dynamic properties mainly of the loss modulus and loss factor and the weaker the frequency dependence the better the accuracy the accuracy is better than 10 if the slope of frequency increase or decrease of loss functions plotted in a log log system is smaller than 0 35 the application of the local k k relations to some experimental data is presented

higher order eigensensitivities based numerical method for the harmonic analysis of viscoelastically damped structures this paper presents an efficient numerical method for the harmonic analysis of viscoelastically damped structural systems characterized by a frequency dependent structural damping matrix making use of the complex mode superposition method departing from the undamped eigensolution the proposed numerical method updates the complex and frequency dependent eigenpair avoiding the solution of a complex eigenproblem for each computational frequency the complex eigenvalues and eigenvectors are updated within the desired tolerance by an adaptive step size control scheme using the first and higher order eigenderivatives the influence on the computation time of the considered number of eigenderivatives and the tolerance is discussed and the efficiency of the proposed numerical method for the harmonic analysis of viscoelastically damped large ordered structural systems is proved finally a practical application is presented where the harmonic response of a constrained layer damping cantilever beam subjected to a base motion is analyzed the complex and frequency dependent eigenvalues and eigenvectors are computed the modal contributions to the total response are determined and the total response is approximated by the complex mode superposition method finally the approximated response is validated with the exact one computed by the direct frequency method and with that experimentally measured copyright 2011 john wiley sons ltd

an approximation algorithm for feedback vertex sets in tournaments we obtain a necessary and sufficient condition in terms of forbidden structures for tournaments to possess the min max relation on packing and covering directed cycles together with strongly polynomial time algorithms for the feedback vertex set problem and the cycle packing problem in this class of tournaments applying the local ratio technique of bar yehuda and even to the forbidden structures we find a 2 5 approximation polynomial time algorithm for the feedback vertex set problem in any tournament

a min max theorem on feedback vertex sets we establish a necessary and sufficient condition for the linear system x hx e x 0 associated with a bipartite tournament to be totally dual integral whereh is the cycle vertex incidence matrix ande is the all one vector the consequence is a min max relation on packing and covering cycles together with strongly polynomial time algorithms for the feedback vertex set problem and the cycle packing problem on the corresponding bipartite tournaments in addition we show that the feedback vertex set problem on general bipartite tournaments isnp complete and approximable within 3 5 based on the min max theorem

relation of bulk to shear loss factor of solid viscoelastic materials the relation between the bulk and shear loss factors of isotropic homogeneous linear solid viscoelastic materials is investigated in this paper by means of the complex modulus concept it is shown that the bulk and shear loss factors can be related through the dynamic poisson s ratio provided that the shear loss is low enough bounds on the ratio of the bulk to shear loss factor are derived and the respective lower bounds are given as a function of the dynamic poisson s ratio the ratio of the bulk to shear loss factor is predicted to decrease with the increase of dynamic poisson s ratio and it is shown that the decrease may obey a simple power law if the poisson s ratio is close to either 0 or 0 5 experimental data on solid polymeric materials are presented which support the theoretical findings

identification of complex moduli and poisson s ratio from measured strains on an impacted bar identification of complex moduli and poisson s ratio from measured strains on an impacted bar

shape and nonrigid motion estimation through physics based synthesis a physics based framework for 3 d shape and nonrigid motion estimation for real time computer vision systems is presented the framework features dynamic models that incorporate the mechanical principles of rigid and nonrigid bodies into conventional geometric primitives through the efficient numerical simulation of lagrange equations of motion the models can synthesize physically correct behaviors in response to applied forces and imposed constraints applying continuous kalman filtering theory a recursive shape and motion estimator that employs the lagrange equations as a system model is developed the system model continually synthesizes nonrigid motion in response to generalized forces that arise from the inconsistency between the incoming observations and the estimated model state the observation forces also account formally for instantaneous uncertainties and incomplete information a riccati procedure updates a covariance matrix that transforms the forces in accordance with the system dynamics and prior observation history experiments involving model fitting and tracking of articulated and flexible objects from noisy 3 d data are described

recovery of elastic parameters of cellular materials by inversion of vibrational data abstract this investigation addresses the inverse problem of the retrieval of the macroscopic young s modulus and poisson ratio of light fluid saturated cellular materials from the measured resonance frequencies of their vibrational response the latter is shown to be generally nonlinear for all except very small excitations in order to remain in the linear regime and thus avoid ambiguities in the characterization of the material we employ lightweight piezoelectric transducers and sensors and a low noise amplifier for the acquisition of dynamic response data the cellular materials under investigation polyurethane melamine are shown to behave as isotropic equivalent homogeneous elastic solids with rayleigh damping equivalent to a rheological model having the same dependence of attenuation on frequency in the frequency band of interest our viscoelastic model is validated by comparing its predictions of the dynamic response of the specimens to the predictions of the more complete biot interaction model the computations with both of these models are carried out by means of 3d finite element fe codes for a variety of specimen shapes and sizes once the simpler viscoelastic model is validated it is then employed to compute the trial resonance frequencies which are the solutions of an eigenvalue problem associated with this model during the inversion process the cost function which is a measure of the discrepancy between the trial and measured resonance frequencies for a number of vibrational modes is constructed and then minimized by the levenberg marquardt optimization algorithm in order to recover the sought for moduli finally a discussion is presented concerning the influence of the size and shape of the specimens as well as the number of employed modes on the uniqueness and quality of the inversion

rank centrality ranking from pairwise comparisons the question of aggregating pairwise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time be it ranking of online gamers e g msr s trueskill system and chess players aggregating social opinions or deciding which product to sell based on transactions in most settings in addition to obtaining a ranking finding scores for each object e g player s rating is of interest for understanding the intensity of the preferences in this paper we propose rank centrality an iterative rank aggregation algorithm for discovering scores for objects or items from pairwise comparisons the algorithm has a natural random walk interpretation over the graph of objects with an edge present between a pair of objects if they are compared the score which we call rank centrality of an object turns out to be its stationary probability under this random walk to study the efficacy of the algorithm we consider the popular bradley terry luce btl model equ

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

kernel and fast algorithm for dense triplet inconsistency we study the parameterized complexity of inferring supertrees from sets of rooted triplets an important problem in phylogenetics for a set l of labels and a dense set t of triplets distinctly leaf labeled by 3 subsets of l we seek a tree distinctly leaf labeled by l and containing all but at most k triplets from t as homeomorphic subtree our results are the first polynomial kernel for this problem with o k 2 labels and a subexponential fixed parameter algorithm running in time o l 4 2 o k 1 3 l o g k

correlation clustering with constrained cluster sizes and extended weights bounds we consider the problem of correlation clustering on graphs with constraints on both the cluster sizes and the positive and negative weights of edges our contributions are twofold first we introduce the problem of correlation clustering with bounded cluster sizes second we extend the regime of weight values for which the clustering may be performed with constant approximation guarantees in polynomial time and apply the results to the bounded cluster size problem

packing circuits in matroids the purpose of this paper is to characterize all matroids m that satisfy the following minimax relation for any nonnegative integral weight function w defined on e m begin aligned rm maximum k m rm has k rm circuits repetition allowed such that each element e rm of m quad quad quad quad quad rm is used at most 2w e rm times by these circuits rm minimum left sum nolimits x in x w x x rm is a collection of elements repetition allowed of m right quad quad quad quad quad left vphantom sum x in x rm such that every circuit in m rm meets x rm at least twice right end aligned our characterization contains a complete solution to a research problem on 2 edge connected subgraph polyhedra posed by cornuejols fonlupt and naddef in 1985 which was independently solved by vandenbussche and nemhauser in vandenbussche and nemhauser j comb optim 9 357 379 2005

feedback vertex sets on restricted bipartite graphs a feedback vertex set fvs in a graph is a subset of vertices whose complement induces a forest finding a minimum fvs is np complete on bipartite graphs but tractable on convex bipartite graphs and on chordal bipartite graphs a bipartite graph is called tree convex if a tree is defined on one part of the vertices such that for every vertex in the other part its neighborhood induces a subtree when the tree is a path a triad or a star the bipartite graph is called convex bipartite triad convex bipartite or star convex bipartite respectively we show that 1 fvs is tractable on triad convex bipartite graphs 2 fvs is np complete on star convex bipartite graphs and on tree convex bipartite graphs where the maximum degree of vertices on the tree is at most three

active learning using smooth relative regret approximations with applications the disagreement coeffcient of hanneke has become a central data independent invariant in proving active learning rates it has been shown in various ways that a concept class with low complexity together with a bound on the disagreement coeffcient at an optimal solution allows active learning rates that are superior to passive learning ones we present a different tool for pool based active learning which follows from the existence of a certain uniform version of low disagreement coeffcient but is not equivalent to it in fact we present two fundamental active learning problems of significant interest for which our approach allows nontrivial active learning bounds however any general purpose method relying on the disagreement coeffcient bounds only fails to guarantee any useful bounds for these problems the applications of interest are learning to rank from pairwise preferences and clustering with side information a k a semi supervised clustering the tool we use is based on the learner s ability to compute an estimator of the difference between the loss of any hypothesis and some fixed pivotal hypothesis to within an absolute error of at most e times the disagreement measure l1 distance between the two hypotheses we prove that such an estimator implies the existence of a learning algorithm which at each iteration reduces its in class excess risk to within a constant factor each iteration replaces the current pivotal hypothesis with the minimizer of the estimated loss difference function with respect to the previous pivotal hypothesis the label complexity essentially becomes that of computing this estimator

a more effective linear kernelization for cluster editing in the np hard cluster editing problem we have as input an undirected graph g and an integer k 0 the question is whether we can transform g by inserting and deleting at most k edges into a cluster graph that is a union of disjoint cliques we first confirm a conjecture by michael fellows iwpec 2006 that there is a polynomial time kernelization for cluster editing that leads to a problem kernel with at most 6k vertices more precisely we present a cubic time algorithm that given a graph g and an integer k 0 finds a graph g and an integer k k such that g can be transformed into a cluster graph by at most k edge modifications iff g can be transformed into a cluster graph by at most k edge modifications and the problem kernel g has at most 6k vertices so far only a problem kernel of 24k vertices was known second we show that this bound for the number of vertices of g can be further improved to 4k vertices finally we consider the variant of cluster editing where the number of cliques that the cluster graph can contain is stipulated to be a constant d 0 we present a simple kernelization for this variant leaving a problem kernel of at most d 2 k d vertices

vertex deletion on split graphs beyond 4 hitting set abstract vertex deletion problems on graphs involve finding a set of minimum number of vertices whose deletion results in a graph with some specific property in a recent study on vertex deletion problems on split graphs it was shown that transforming a split graph to a block graph or a threshold graph using minimum number of vertex deletions is np hard we call the decision version of these problems as split to block vertex deletion sbvd and split to threshold vertex deletion stvd respectively in this paper we study the parameterized complexity of these problems with the number of vertex deletions k as a parameter these problems are implicit 4 hitting set and thus admit an algorithm with running time o 3 0755 k a kernel with o k 3 vertices and a 4 approximation algorithm in this paper we exploit the structural properties of the concerned graph classes and obtain a kernel for sbvd with o k 2 vertices and fpt algorithms for sbvd and stvd with running times o 2 3028 k and o 2 7913 k respectively we further give improved approximation algorithms for sbvd and stvd

clustering algorithms for chains we consider the problem of clustering a set of chains to k clusters a chain is a totally ordered subset of a finite set of items chains are an intuitive way to express preferences over a set of alternatives as well as a useful representation of ratings in situations where the item specific scores are either difficult to obtain too noisy due to measurement error or simply not as relevant as the order that they induce over the items first we adapt the classical k means for chains by proposing a suitable distance function and a centroid structure we also present two different approaches for mapping chains to a vector space the first one is related to the planted partition model while the second one has an intuitive geometrical interpretation finally we discuss a randomization test for assessing the significance of a clustering to this end we present an mcmc algorithm for sampling random sets of chains that share certain properties with the original data the methods are studied in a series of experiments using real and artificial data results indicate that the methods produce interesting clusterings and for certain types of inputs improve upon previous work on clustering algorithms for orders

comparing and aggregating partially resolved trees partially resolved that is non binary trees arise frequently in the analysis of species evolution non binary nodes also called multifurcations must be treated carefully since they can be interpreted as reflecting either lack of information or actual evolutionary history while several distance measures exist for comparing trees none of them deal explicitly with this dichotomy here we introduce two kinds of distance measures between rooted and unrooted partially resolved phylogenetic trees over the same set of species the measures address multifurcations directly for rooted trees the measures are based on the topologies the input trees induce on triplets that is on three element subsets of the set of species for unrooted trees the measures are based on quartets four element subsets the first class of measures are parametric distances where there is a parameter that weighs the difference between an unresolved triplet quartet topology and a resolved one the second class of measures are based on the hausdorff distance where each tree is viewed as a set of all possible ways in which the tree can be refined to eliminate unresolved nodes we give efficient algorithms for computing parametric distances and give conditions under which hausdorff distances can be calculated approximately in polynomial time additionally we i derive the expected value of the parametric distance between two random trees ii characterize the conditions under which parametric distances are near metrics or metrics iii study the computational and algorithmic properties of consensus tree methods based on the measures and iv analyze the interrelationships among hausdorff and parametric distances

a variational method for identification of viscoelastic parameters from experimental data an identification method based on a variational algorithm is proposed for the determination of the viscoelastic properties of plates from a set of experimental data a pulse transmission configuration with parametric sources is used for the experimental measurements whereby two electrically mixed high frequency pulses are combined to generate a low frequency narrow beam signal to minimize the edge diffraction effect from a finite sized panel the variational method is based on an optimization technique adapted from an approach normally used in dynamics for the identification of motion parameters the fluid panel fluid system is initially represented by a transmission matrix model the model and the known experimental data are then used to build an optimization functional to be minimized by means of a collocation method based on the hamilton principle analogy the plate viscoelastic parameters are then calculated by application of well established numerical procedures to the resulting set of integral differential equations

a new correlation coefficient for comparing and aggregating non strict and incomplete rankings abstract we introduce a correlation coefficient that is designed to deal with a variety of ranking formats including those containing non strict i e with ties and incomplete i e unknown preferences the correlation coefficient is designed to enforce a neutral treatment of incompleteness whereby no assumptions are made about individual preferences involving unranked objects the new measure which can be regarded as a generalization of the seminal kendall tau correlation coefficient is proven to satisfy a set of metric like axioms and to be equivalent to a recently developed ranking distance function associated with kemeny aggregation in an effort to further unify and enhance both robust ranking methodologies this work proves the equivalence of an additional distance and correlation coefficient pairing in the space of non strict incomplete rankings these connections induce new exact optimization methodologies a specialized branch and bound algorithm and an exact integer programming formulation moreover the bridging of these complementary theories reinforces the singular suitability of the featured correlation coefficient to solve the general consensus ranking problem the latter premise is bolstered by an accompanying set of experiments on random instances which are generated via a herein developed sampling technique connected with the classic mallows distribution of ranking data associated experiments with the branch and bound algorithm demonstrate that as data becomes noisier the featured correlation coefficient yields relatively fewer alternative optimal solutions and that the aggregate rankings tend to be closer to an underlying ground truth shared by a majority

circular convex bipartite graphs a feedback vertex set is a subset of vertices such that the removal of this subset renders the remaining graph cycle free the weight of a feedback vertex set is the sum of weights of its vertices finding a minimum weighted feedback vertex set is tractable for convex bipartite graphs but np complete even for unweighted bipartite graphs in a circular convex convex respectively bipartite graph there is a circular linear respectively ordering defined on one class of vertices such that for every vertex in another class the neighborhood of this vertex is a circular arc an interval respectively the minimum weighted feedback vertex set problem is shown tractable for circular convex bipartite graphs in this paper by making a cook reduction i e polynomial time turing reduction for this problem from circular convex bipartite graphs to convex bipartite graphs

frequency dependences of complex moduli and complex poisson s ratio of real solid materials abstract the concept of a complex modulus of elasticity is a powerful and widely used tool for characterizing the linear dynamic elastic and damping properties of solid materials in the frequency domain it is shown in this paper that typical characters of frequency dependences of all complex moduli shear young s etc and complex poisson s ratios of real solid materials can be determined by transforming the causal and real relaxation and creep responses respectively from the time domain into the frequency domain even without having to specify the processes of relaxation and creep it is proved that all dynamic moduli monotonically increase and the dynamic poisson s ratio monotonically decreases with increasing frequency and all respective loss factors pass through at least one maximum these frequency dependences are generally valid for any real solid material regardless of the actual damping mechanisms some experimental results are presented and interpreted in the light of the theory the usefulness of theoretical predictions in materials engineering measurements of dynamic properties and in modelling dynamic behaviour is discussed

cost optimal constrained correlation clustering via weighted partial maximum satisfiability abstract integration of the fields of constraint solving and data mining and machine learning has recently been identified within the ai community as an important research direction with high potential this work contributes to this direction by providing a first study on the applicability of state of the art boolean optimization procedures to cost optimal correlation clustering under constraints in a general similarity based setting we develop exact formulations of the correlation clustering task as maximum satisfiability maxsat the optimization version of the boolean satisfiability sat problem for obtaining cost optimal clusterings we apply a state of the art maxsat solver for solving the resulting maxsat instances optimally resulting in cost optimal clusterings we experimentally evaluate the maxsat based approaches to cost optimal correlation clustering both on the scalability of our method and the quality of the clusterings obtained furthermore we show how the approach extends to constrained correlation clustering where additional user knowledge is imposed as constraints on the optimal clusterings of interest we show experimentally that added user knowledge allows clustering larger datasets and at the same time tends to decrease the running time of our approach we also investigate the effects of maxsat level preprocessing symmetry breaking and the choice of the maxsat solver on the efficiency of the approach

linear programming based approximation algorithms for feedback set problems in bipartite tournaments we consider the feedback vertex set and feedback arc set problems on bipartite tournaments we improve on recent results by giving a 2 approximation algorithm for the feedback vertex set problem we show that this result is the best that we can attain when using optimal solutions to a certain linear program as a lower bound on the optimal value for the feedback arc set problem on bipartite tournaments we show that a recent 4 approximation algorithm proposed by gupta 2008 8 is incorrect we give an alternative 4 approximation algorithm based on an algorithm for the feedback arc set on non bipartite tournaments given by van zuylen and williamson 2009 14

an almost linear time algorithm for graph realization given a 0 1 matrix m the graph realization problem for m is to find a tree t such that the columns of m are incidence vectors of paths in t or to show that no such t exists an algorithm is presented for this problem the time complexity of which is very nearly linear in the number of ones in m

correlation clustering with noisy partial information in this paper we propose and study a semi random model for the correlation clustering problem on arbitrary graphs g we give two approximation algorithms for correlation clustering instances from this model the first algorithm finds a solution of value 1 opt cost o nlog 3 n with high probability where opt cost is the value of the optimal solution for every 0 the second algorithm finds the ground truth clustering with an arbitrarily small classific ation error under some additional assumptions on the instance