asymptotic upper curvature bounds in coarse geometry we define a notion of an asymptotic upper curvature bound for gromov hyperbolic metric spaces that is invariant under rough isometries and examine the basic properties of this concept

consistent labeling problems and their algorithms expected complexities and theory based heuristics a new parameter is introduced to characterize a type of search problem of broad relevance in artificial intelligence operations research and symbolic logic this parameter which we call inter variable compatibility is particularly important in that complexity analyses incorporating it are able to capture the dependence of problem complexity on search order used by an algorithm thus compatibility based theories can provide a theoretical basis for the extraction of heuristics for choosing good search orderings a long sought goal for such problems since it can lead to significant savings during search we carry out expected complexity analyses for the traditional backtrack algorithm as well as for two more recent algorithms that have been found empirically to be significant improvements forward checking and word wise forward checking we extract compatibility based ordering heuristics from the theory for forward checking preliminary experimental results are presented showing the large savings that result from their use similar savings can be expected for other algorithms when heuristics taking account of inter variable compatibilities are used our compatibility based theories also provide a more precise way of predicting which algorithm is best for a given problem

identification of continuous time models the paper considers the problem of estimation of the transfer function of a continuous time dynamic system in the presence of colored noise the author introduces an operator transformation that allows for keeping a continuous time parametrization whereas the parameter estimation can be made by means of a discrete time maximum likelihood algorithm a comparison is made between the performance of the new method in comparison with a standard identification of an armax model the method is useful in cases where it is important to estimate the coefficients of a continuous time transfer function and where it is important to maintain a physical interpretation of the transfer function results

a sampling theory approach for continuous arma identification the problem of estimating continuous domain autoregressive moving average processes from sampled data is considered the proposed approach incorporates the sampling process into the problem formulation while introducing exponential models for both the continuous and the sampled processes we derive an exact evaluation of the discrete domain power spectrum using exponential b splines and further suggest an estimation approach that is based on digitally filtering the available data the proposed functional which is related to whittle s likelihood function exhibits several local minima that originate from aliasing the global minimum however corresponds to a maximum likelihood estimator regardless of the sampling step experimental results indicate that the proposed approach closely follows the cramer rao bound for various aliasing configurations

theories of causal ordering abstract this paper is a response to iwasaki and simon 14 which criticizes de kleer and brown 8 we argue that many of their criticisms particularly concerning causality modeling and stability originate from the difference of concerns between engineering and economics our notion of causality arises from considering the interconnections of components not equations when no feedback is present the ordering produced by our qualitative physics is similar to theirs however when feedback is present our qualitative physics determines a causal ordering around feedback loops as well causal ordering is a general technique not only applicable to qualitative reasoning therefore we also explore the relationship between causal ordering and propagation of constraints upon which the methods of qualitative physics are based

guaranteed robust nonlinear minimax estimation minimax parameter estimation aims at characterizing the set of all values of the parameter vector that minimize the largest absolute deviation between the experimental data and the corresponding model outputs it is well known however to be extremely sensitive to outliers in the data resulting e g of sensor failures in this paper a new method is proposed to robustify minimax estimation by allowing a prespecified number of absolute deviations to become arbitrarily large without modifying the estimates by combining tools of interval analysis and constraint propagation it becomes possible to compute the corresponding minimax estimates in an approximate but guaranteed way even when the model output is nonlinear in its parameters the method is illustrated on a problem where the parameters are not globally identifiable which demonstrates its ability to deal with the case where the minimax solution is not unique

on the role of exponential splines in image interpolation a sobolev reproducing kernel hilbert space approach to image interpolation is introduced the underlying kernels are exponential functions and are related to stochastic autoregressive image modeling the corresponding image interpolants can be implemented effectively using compactly supported exponential b splines a tight l2 upper bound on the interpolation error is then derived suggesting that the proposed exponential functions are optimal in this regard experimental results indicate that the proposed interpolation approach with properly tuned signal dependent weights outperforms currently available polynomial b spline models of comparable order furthermore a unified approach to image interpolation by ideal and nonideal sampling procedures is derived suggesting that the proposed exponential kernels may have a significant role in image modeling as well our conclusion is that the proposed sobolev based approach could be instrumental and a preferred alternative in many interpolation tasks

some fundamental properties of local constraint propagation abstract in this paper we give results about termination of local constraint propagation algorithms under possibly infinite variable coverings the locally consistent arc consistent solution of a constraint problem if such a solution exists is shown to be unique and a sufficient condition on propagations is given to find that solution even in the presence of arbitrary infinite variable coverings

temporal constraint networks abstract this paper extends network based methods of constraint satisfaction to include continuous variables thus providing a framework for processing temporal constraints in this framework called temporal constraint satisfaction problem tcsp variables represent time points and temporal information is represented by a set of unary and binary constraints each specifying a set of permitted intervals the unique feature of this framework lies in permitting the processing of metric information namely assessments of time differences between events we present algorithms for performing the following reasoning tasks finding all feasible times that a given event can occur finding all possible relationships between two given events and generating one or more scenarios consistent with the information provided we distinguish between simple temporal problems stps and general temporal problems the former admitting at most one interval constraint on any pair of time points we show that the stp which subsumes the major part of vilain and kautz s point algebra can be solved in polynomial time for general tcsps we present a decomposition scheme that performs the three reasoning tasks considered and introduce a variety of techniques for improving its efficiency we also study the applicability of path consistency algorithms as preprocessing of temporal problems demonstrate their termination and bound their complexities

estimation of continuous time stochastic signals from sample covariances the problem of estimating the parameters in stochastic continuous time signals represented as continuous time autoregressive moving average arma processes from discrete time data is considered the proposed solution is to fit the covariance function of the process parameterized by the unknown parameters to sample covariances it is shown that the method is consistent and an expression for the approximate covariance matrix of the estimated parameter vector is derived the derived variances are compared with empirical variances from a monte carlo simulation and with the cramer rao bound it turns out that the variances are close to the cramer rao bound for certain choices of the sampling interval and the number of covariance elements used in the criterion function

the complexity of some polynomial network consistency algorithms for constraint satisfaction problems abstract constraint satisfaction problems play a central role in artificial intelligence a class of network consistency algorithms for eliminating local inconsistencies in such problems has previously been described we analyze the time complexity of several node arc and path consistency algorithms and prove that arc consistency is achievable in time linear in the number of binary constraints the waltz filtering algorithm is a special case of the arc consistency algorithm in the edge labelling computational vision application the constraint graph is planar and so the time complexity is linear in the number of variables

fitting continuous arma models to unequally spaced spatial data abstract methods for fitting continuous spatial autoregressive moving average arma models to unequally spaced observations in two dimensions are reviewed and extended these are models with rational two dimensional spectra assuming gaussian input noise and observational errors maximum likelihood methods are used to estimate the arma parameters and the regression coefficients of the deterministic trend when the number of observations is too large for exact maximum likelihood estimation approximate maximum likelihood estimation is used based on nearest neighbors comparisons of nearest neighbor methods with exact likelihood methods are presented predictions of the height of the field at unobserved points can be calculated with confidence intervals

fast algorithms for solving path problems let g v e be a directed graph with a distinguished source vertex s the single source path expression problem is to find for each vertex v a regular expression p s v which represents the set of all paths in g from s to v a solution to this problem can be used to solve shortest path problems solve sparse systems of linear equations and carry out global flow analysis we describe a method to compute path expressions by dividing g into components computing path expressions on the components by gaussian elimination and combining the solutions this method requires o m alpha m n time on a reducible flow graph where n is the number of vertices in g m is the number of edges in g and alpha is a functional inverse of ackermann s function the method makes use of an algorithm for evaluating functions defined on paths in trees a simplified version of the algorithm which runs in o m log n time on reducible flow graphs is quite easy to implement and efficient in practice

cardinal exponential splines part i theory and filtering algorithms causal exponentials play a fundamental role in classical system theory starting from those elementary building blocks we propose a complete and self contained signal processing formulation of exponential splines defined on a uniform grid we specify the corresponding b spline basis functions and investigate their reproduction properties green function and exponential polynomials we also characterize their stability riesz bounds we show that the exponential b spline framework allows an exact implementation of continuous time signal processing operators including convolution differential operators and modulation by simple processing in the discrete b spline domain we derive efficient filtering algorithms for multiresolution signal extrapolation and approximation extending earlier results for polynomial splines finally we present a new asymptotic error formula that predicts the magnitude and the nth order decay of the l sub 2 approximation error as a function of the knot spacing t

on the unique identification of continuous time autoregressive models from sampled data in this work we investigate the relationship between continuous time autoregressive ar models and their sampled version we consider uniform sampling and derive criteria for uniquely determining the continuous time parameters from sampled data the model order is assumed to be known we achieve this by removing a set of measure zero from the collection of all ar models and by investigating the asymptotic behavior of the remaining set of autocorrelation functions we provide necessary and sufficient conditions for uniqueness of general ar models and we demonstrate the usefulness of this result by considering particular examples we further exploit our theory and introduce an estimation algorithm that recovers continuous time ar parameters from sampled data regardless of the sampling interval we demonstrate the usefulness of our algorithm for various gaussian and non gaussian ar processes

constraint reasoning based on interval arithmetic the tolerance propagation approach abstract interval constraint satisfaction interval labeling systems have traditionally been based on local waltz filtering techniques that cannot in general determine global solutions in contrast this paper documents a related technique tolerance propagation tp that generalizes the idea of numerical exact value propagation into interval propagation in tp consistency techniques based on the topology of the constraint net can be combined with techniques of interval arithmetic in a new fruitful way in particular by tp it is possible to determine global solutions for interval constraint satisfaction problems with arbitrary accuracy and without losing all attractions of simple local computations

network based heuristics for constraint satisfaction problems abstract many ai tasks can be formulated as constraint satisfaction problems csp i e the assignment of values to variables subject to a set of constraints while some csps are hard those that are easy can often be mapped into sparse networks of constraints which in the extreme case are trees this paper identifies classes of problems that lend themselves to easy solutions and develops algorithms that solve these problems optimally the paper then presents a method of generating heuristic advice to guide the order of value assignments based on both the sparseness found in the constraint network and the simplicity of tree structured csps the advice is generated by simplifying the pending subproblems into trees counting the number of consistent solutions in each simplified subproblem and comparing these counts to decide among the choices pending in the original problem

the essence of constraint propagation we show that several constraint propagation algorithms also called local consistency consistency enforcing waltz filtering or narrowing algorithms are instances of algorithms that deal with chaotic iteration to this end we propose a simple abstract framework that allows us to classify and compare these algorithms and to establish in a uniform way their basic properties

querying temporal and spatial constraint networks in ptime we start with the assumption that temporal and spatial knowledge usually captured by constraint networks can be represented and queried more effectively by using the scheme of indefinite constraint databases because query evaluation in this scheme is in general a hard computational problem we seek tractable instances of query evaluation we assume that we have a class of constraints c with some reasonable computational and closure properties the computational properties of interest are that the satisfiability problem and an appropriate version of the variable elimination problem for c should be solvable in ptime under this assumption we exhibit general classes of indefinite constraint databases and first order modal queries for which query evaluation can be done with ptime data complexity we then search for tractable instances of c among the subclasses of horn disjunctive linear constraints over the rationals from previous research we know that the satisfiability problem for horn disjunctive linear constraints is solvable in ptime but not the variable elimination problem thus we try to discover subclasses of horn disjunctive linear constraints with tractable variable elimination problems the class of utvpi constraints is the largest class that we show to have this property finally we restate our general tractability results with c ranging over the newly discovered tractable classes interesting tractable query answering problems for indefinite temporal and spatial constraint databases are identified in this way we close our complexity analysis by precisely outlining the frontier between tractable and possibly intractable query answering problems

shintani barnes zeta and gamma functions abstract we show that shintani s work on multiple zeta and gamma functions can be simplified and extended by exploiting difference equations we re prove many of shintani s formulas and prove several new ones among the latter is a generalization to the shintani barnes gamma functions of raabe s 1843 formula 0 1 log x dx log 2 and a further generalization to the shintani zeta functions these explicit formulas can be interpreted as vanishing period integral side conditions for the ladder of difference equations obeyed by the multiple gamma and zeta functions we also relate barnes triple gamma function to the elliptic gamma function appearing in connection with certain integrable systems

computing stochastic continuous time models from arma models some algorithms for computing the underlying continuous time stochastic model from a sampled arma model are presented three algorithms are given all having a modest computational complexity the properties of the algorithms are analysed and also illustrated by means of numerical examples

cardinal exponential splines part ii think analog act digital by interpreting the green function reproduction property of exponential splines in signal processing terms we uncover a fundamental relation that connects the impulse responses of allpole analog filters to their discrete counterparts the link is that the latter are the b spline coefficients of the former which happen to be exponential splines motivated by this observation we introduce an extended family of cardinal splines the generalized e splines to generalize the concept for all convolution operators with rational transfer functions we construct the corresponding compactly supported b spline basis functions which are characterized by their poles and zeros thereby establishing an interesting connection with analog filter design techniques we investigate the properties of these new b splines and present the corresponding signal processing calculus which allows us to perform continuous time operations such as convolution differential operators and modulation by simple application of the discrete version of these operators in the b spline domain in particular we show how the formalism can be used to obtain exact discrete implementations of analog filters finally we apply our results to the design of hybrid signal processing systems that rely on digital filtering to compensate for the nonideal characteristics of real world analog to digital a to d and d to a conversion systems

commonsense reasoning about causality deriving behavior from structure abstract this paper presents a qualitative reasoning method for predicting the behavior of mechanisms characterized by continuous time varying parameters the structure of a mechanism is described in terms of a set of parameters and the constraints that hold among them essentially a qualitative differential equation the qualitative behavior description consists of a discrete set of time points at which the values of the parameters are described in terms of ordinal relations and directions of change the behavioral description or envisionment is derived by two sets of rules propagation rules which elaborate the description of the current time point and prediction rules which determine what is known about the next qualitatively distinct state of the mechanism a detailed example shows how the envisionment method can detect a previously unsuspected landmark point at which the system is in stable equilibrium

warped products admitting a curvature bound abstract this paper completes a fundamental construction in alexandrov geometry previously we gave a new construction of metric spaces with curvature bounds either above or below namely warped products with intrinsic metric space base and fiber and with possibly vanishing warping functions thereby extending the classical cone and suspension constructions from interval base to arbitrary base and furthermore encompassing gluing constructions this paper proves the converse namely all conditions of the theorems are necessary note that in the cone construction both the construction and its converse are widely used we also show that our theorems for curvature bounded above and below respectively are dual we give the first systematic development of basic properties of warped products of metric spaces with possibly vanishing warping functions including new properties

maximum likelihood estimation of linear continuous time long memory processes with discrete time data summary we develop a new class of time continuous autoregressive fractionally integrated moving average carfima models which are useful for modelling regularly spaced and irregularly spaced discrete time long memory data we derive the autocovariance function of a stationary carfima model and study maximum likelihood estimation of a regression model with carfima errors based on discrete time data and via the innovations algorithm it is shown that the maximum likelihood estimator is asymptotically normal and its finite sample properties are studied through simulation the efficacy of the approach proposed is demonstrated with a data set from an environmental study

hyperbolicity cat 1 spaces and the ptolemy inequality using a four points inequality for the boundary of cat 1 spaces we study the relation between gromov hyperbolic spaces and cat 1 spaces

local search with constraint propagation and conflict based heuristics search algorithms for solving csp constraint satisfaction problems usually fall into one of two main families local search algorithms and systematic algorithms both families have their advantages designing hybrid approaches seems promising since those advantages may be combined into a single approach in this paper we present a new hybrid technique it performs a local search over partial assignments instead of complete assignments and uses filtering techniques and conflict based techniques to efficiently guide the search this new technique benefits from both classical approaches a priori pruning of the search space from filtering based search and possible repair of early mistakes from local search we focus on a specific version of this technique tabu decision repair experiments done on open shop scheduling problems show that our approach competes well with the best highly specialized algorithms

search rearrangement backtracking and polynomial average time the average time required for simple search rearrangement backtracking is compared with that for ordinary backtracking when each algorithm is used to find all solutions for random conjunctive normal form predicates the sets of random predicates are characterized by v the number of variables t v the number of clauses and p v the probability that a literal appears in clause for large v if vp v 1n 2 there is a difficult region where the average number of solutions per problem is exponentially small but backtracking requires an exponentially large time the difficult region for search rearrangement backtracking is only slightly smaller than the difficult region for ordinary backtracking it is conjectured that search rearrangement backtracking is exponentially faster than ordinary backtracking for nearly all of the difficult region it is proved that there is no major advantage in using search rearrangement backtracking outside of the difficult region

local consistency for extended csps we extend the framework of constraint satisfaction problems to make it more suitable for applicable to modern constraint programming languages where both constraint satisfaction and constraint solving have a role some rough principles for local consistency conditions in the extended framework are developed appropriate notions of local consistency are formulated and relationships between the various consistency conditions are established

frequency domain identification of continuous time arma models from sampled data the subject of this paper is the direct identification of continuous time autoregressive moving average carma models the topic is viewed from the frequency domain perspective which then turns the reconstruction of the continuous time power spectral density ct psd into a key issue the first part of the paper therefore concerns the approximate estimation of the ct psd from uniformly sampled data under the assumption that the model has a certain relative degree the approach has its point of origin in the frequency domain whittle likelihood estimator the discrete or continuous time spectral densities are estimated from equidistant samples of the output for low sampling rates the discrete time spectral density is modeled directly by its continuous time spectral density using the poisson summation formula in the case of rapid sampling the continuous time spectral density is estimated directly by modifying its discrete time counterpart

variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem abstract practical constraint satisfaction problems csps such as design of integrated circuits or scheduling generally entail large search spaces with hundreds or even thousands of variables each with hundreds or thousands of possible values often only a very tiny fraction of all these possible assignments participates in a satisfactory solution this article discusses techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable in the csp literature these techniques are commonly referred to as variable and value ordering heuristics our investigation is conducted in the job shop scheduling domain we show that in contrast with problems studied earlier in the csp literature generic variable and value heuristics do not perform well in this domain this is attributed to the difficulty of these heuristics to properly account for the tightness of constraints and or the connectivity of the constraint graphs induced by job shop scheduling csps a new probabilistic framework is introduced that better captures these key aspects of the job shop scheduling search space empirical results show that variable and value ordering heuristics derived within this probabilistic framework often yield significant improvements in search efficiency and significant reductions in the search time required to obtain a satisfactory solution the research reported in this article was the first one along with the work of keng and yun 1989 to use the csp problem solving paradigm to solve job shop scheduling problems the suite of benchmark problems it introduced has been used since then by a number of other researchers to evaluate alternative techniques for the job shop scheduling csp the article briefly reviews some of these more recent efforts and shows that our variable and value ordering heuristics remain quite competitive

characterizing complete operatorname cat kappa spaces kappa 0 with geodesic boundary we investigate the bourdon and hamenstadt boundaries of complete cat spaces 0 and characterize those with geodesic hamenstadt boundary up to isometry

arc consistency for continuous variables reference lia article 1994 001doi 10 1016 0004 3702 94 90022 1view record in web of science record created on 2006 12 13 modified on 2017 05 12

combining qualitative and quantitative constraints in temporal reasoning abstract this paper presents a general model for temporal reasoning that is capable of handling both qualitative and quantitative information this model allows the representation and processing of many types of constraints discussed in the literature to date including metric constraints restricting the distance between time points and qualitative disjunctive constraints specifying the relative position of temporal objects reasoning tasks in this unified framework are formulated as constraint satisfaction problems and are solved by traditional constraint satisfaction techniques such as backtracking and path consistency new classes of tractable problems are characterized involving qualitative networks augmented by quantitative domain constraints some of which can be solved in polynomial time using arc and path consistency

adaptive image resizing based on continuous domain stochastic modeling we introduce an adaptive continuous domain modeling approach to texture and natural images the continuous domain image is assumed to be a smooth function and we embed it in a parameterized sobolev space we point out a link between sobolev spaces and stochastic auto regressive models and exploit it for optimally choosing sobolev parameters from available pixel values to this aim we use exact continuous to discrete mapping of the auto regressive model that is based on symmetric exponential splines the mapping is computationally efficient and we exploit it for maximizing an approximated gaussian likelihood function we account for non gaussian levy type processes by deriving a more robust estimator that is based on the sample auto correlation sequence both estimators use multiple initialization values for overcoming the local minima structure of the fitting criteria experimental image resizing results indicate that the auto correlation criterion can cope better with non gaussian processes and model mismatch our work demonstrates the importance of the auto correlation function in adaptive image interpolation and image modeling tasks and we believe it is instrumental in other image processing tasks as well

em based identification of continuous time arma models from irregularly sampled data abstract in this paper we present a novel algorithm for identifying continuous time autoregressive moving average models utilizing irregularly sampled data the proposed algorithm is based on the expectation maximization algorithm and obtains maximum likelihood estimates the proposed algorithm shows a fast convergence rate good robustness to initial values and desirable estimation accuracy comparisons are made with other algorithms in the literature via numerical examples

constraint propogation techniques for the disjunctive scheduling problem abstract constraint propagation is an elementary method for reducing the search space of combinatorial search and optimization problems which has become more and more important in the last decades the basic idea of constraint propagation is to detect and remove inconsistent variable assignments that cannot participate in any feasible solution through the repeated analysis and evaluation of the variables domains and constraints describing a specific problem instance the contribution of this paper is twofold the first contribution is a description of efficient constraint propagation methods also known as consistency tests for the disjunctive scheduling problem dsp which is a generalization of the classical job shop scheduling problem jsp applying an elementary constraint based approach involving a limited number of search variables we will derive consistency tests that ensure 3 b consistency we will further present and analyze both new and classical consistency tests which to some extent are generalizations of the aforementioned consistency tests involving a higher number of variables but still can be implemented efficiently with a polynomial time complexity further the concepts of energetic reasoning and shaving are analyzed and discussed the other contribution is a classification of the consistency tests derived according to the domain reduction achieved the particular strength of using consistency tests is based on their repeated application so that the knowledge derived is propagated i e reused for acquiring additional knowledge the deduction of this knowledge can be described as the computation of a fixed point since this fixed point depends upon the order of the application of the tests we first derive a necessary condition for its uniqueness we then develop a concept of dominance which enables the comparison of different consistency tests as well as a simple method for proving dominance an extensive comparison of all consistency tests is given quite surprisingly we will find out that some apparently stronger consistency tests are subsumed by apparently weaker ones at the same time an open question regarding the effectiveness of energetic reasoning is answered

simple linear time algorithms to test chordality of graphs test acyclicity of hypergraphs and selectively reduce acyclic hypergraphs chordal graphs arise naturally in the study of gaussian elimination on sparse symmetric matrices acyclic hypergraphs arise in the study of relational data bases rose tarjan and lueker siam j c

ftclogic fuzzy temporal constraint logic abstract in this paper we present ftclogic a formal first order logic that can manage fuzzy temporal constraints between variables efficiently in this logic the use of explicit temporal axioms is unnecessary and therefore the deduction mechanism doesn t slow down for this reason ftclogic has an immediate precedent the extended fuzzy temporal constraint logic or eftcl however while eftcl is based on timed possibilistic logic ftclogic uses the possibilistic logic to formulate an original semantics according to its syntax in fact ftclogic defines both syntax and semantics from a powerful combination of two formalisms the possibilistic logic and the fuzzy temporal constraints networks ftclogic has provided the basis for the creation of a fuzzy temporal prolog prologic which is implemented through haskell and which is currently undergoing evaluation

on the computation of the cramer rao bound for arma parameter estimation the cramer rao lower bound crlb provides a useful tool for evaluating the performance of parameter estimation techniques several techniques for the computation of the asymptotic form of the crlb for arma models are presented it is shown that the asymptotic crlb can be expressed as an explicit function of the model parameters

on the foundations of relaxation labeling processes a large class of problems can be formulated in terms of the assignment of labels to objects frequently processes are needed which reduce ambiguity and noise and select the best label among several possible choices relaxation labeling processes are just such a class of algorithms they are based on the parallel use of local constraints between labels this paper develops a theory to characterize the goal of relaxation labeling the theory is founded on a definition of con sistency in labelings extending the notion of constraint satisfaction in certain restricted circumstances an explicit functional exists that can be maximized to guide the search for consistent labelings this functional is used to derive a new relaxation labeling operator when the restrictions are not satisfied the theory relies on variational cal culus it is shown that the problem of finding consistent labelings is equivalent to solving a variational inequality a procedure nearly identical to the relaxation operator derived under restricted circum stances serves in the more general setting further a local convergence result is established for this operator the standard relaxation labeling formulas are shown to approximate our new operator which leads us to conjecture that successful applications of the standard methods are explainable by the theory developed here observations about con vergence and generalizations to higher order compatibility relations are described

deciding linear inequalities by computing loop residues v r pratt has shown that the real and integer feastbdlty of sets of linear mequallues of the form x y c can be decided quickly by examining the loops m certain graphs pratt s method is generahzed first to real feaslbdlty of mequahues m two variables and arbitrary coefficients and ultimately to real feaslbdlty of arbitrary sets of hnear mequahtles the method is well suited to apphcatlons m program verification

technical communique interval constraint propagation with application to bounded error estimation for a large class of bounded error estimation problems the posterior feasible set s for the parameters can be defined by nonlinear inequalities the set inversion approach combines classical interval analysis with branch and bound algorithms to characterize s unfortunately as bisections have to be done in all directions of the parameter space this approach is limited to problems involving a small number of parameters techniques based on interval constraint propagation make it possible to drastically reduce the number of bisections in this paper these techniques are combined with set inversion to bracket s between inner and outer subpavings union of nonoverlapping boxes when only interested in the feasible intervals for the parameters the set inversion approach becomes inefficient and a new algorithm able to compute these intervals is given this algorithm uses a new interval based local research to compute the smallest box that contains s it is then compared with existing methods on an example taken from the literature

quasiconformal mappings on the heisenberg group on etudie les applications conformes et quasi conformes sur le groupe de heisenberg la metrique invariante a gauche naturelle sur cet espace est non isotrope sous des dilatations et ne peut se decrire par une metrique de riemann ceci engendre de nouveaux phenomenes

enhancement schemes for constraint processing backjumping learning and cutset decomposition abstract researchers in the areas of constraint satisfaction problems logic programming and truth maintenance systems have suggested various schemes for enhancing the performance of the backtracking algorithm this paper defines and compares the performance of three such schemes backjumping learning and cycle cutset the backjumping and the cycle cutset methods work best when the constraint graph is sparse while the learning scheme mostly benefits problem instances with dense constraint graphs an integrated strategy is described which utilizes the distinct advantages of each scheme experiments show that in hard problems the average improvement realized by the integrated scheme is 20 25 higher than any of the individual schemes

limit results for sampled systems properties of discrete time systems obtained by sampling continuous time systems are described by introducing prefilters we can treat different ways of sampling within one framework results on the convergence of poles and zeros of transfer functions and noise filters as the sampling interval tends to zero are given these results are generalizations of the results of astrom hagander and sternby 1984 on the convergence of poles and zeros for zero order hold sampled transfer functions sampled noise models are also analysed knowledge of these properties is very important in for example discrete time simulations of continuous time systems and identification of continuous time models based on discrete time measurements

the farrell jones conjecture for the solvable baumslag solitar groups in this paper we prove the farrell jones conjecture for the solvable baumslag solitar groups with coefficients in an additive category we also extend our results to groups of the form z 1 p semidirect product with any virtually cyclic group where p is a prime number

the logic of constraint satisfaction the constraint satisfaction problem csp formalization has been a productive tool within artificial intelligence and related areas the finite csp fcsp framework is presented here as a restricted logical calculus within a space of logical representation and reasoning systems fcsp is formulated in a variety of logical settings theorem proving in first order predicate calculus propositional theorem proving and hence sat the prolog and datalog approaches constraint network algorithms a logical interpreter for networks of constraints the constraint logic programming clp paradigm and propositional model finding and hence sat again several standard and some not so standard logical methods can therefore be used to solve these problems by doing this we obtain a specification of the semantics of the common approaches this synthetic treatment also allows algorithms and results from these disparate areas to be imported and specialized to fcsp the special properties of fcsp are exploited to achieve for example completeness and to improve efficiency it also allows export to the related areas by casting csp both as a generalization of fcsp and as a specialization of clp it is observed that some but not all fcsp techniques lift to csp and perhaps thereby to clp various new connections are uncovered in particular between the proof finding approaches and the alternative model finding approaches that have arisen in depiction and diagnosis applications

causality in device behavior abstract this paper shows how formal characterizations of causality and of the method of comparative statics long used in econometrics thermodynamics and other domains can be applied to clarify and make rigorous the qualitative causal calculus recently proposed by de kleer and brown 2 the formalization shows exactly what assumptions are required to carry out causal analysis of a system of interdependent variables in equilibrium and to propagate disturbances through such a system the intuitive concepts of causality captured by de kleer and brown provide a rough approximation to the standard analytic techniques that are used in the treatment of simultaneous algebraic and differential equations

tractable disjunctions of linear constraints basic results and applications to temporal reasoning we study the problems of deciding consistency and performing variable elimination for disjunctions of linear inequalities and disequations with at most one inequality per disjunction this new class of constraints extends the class of generalized linear constraints originally studied by lassez and mcaloon we show that deciding consistency of a set of constraints in this class can be done in polynomial time we also present a variable elimination algorithm which is similar to fourier s algorithm for linear inequalities finally we use these results to provide new temporal reasoning algorithms for the ord horn subclass of allen s interval formalism we also show that there is no low level of local consistency that can guarantee global consistency for the ord horn subclass this property distinguishes the ord horn subclass from the pointizable subclass for which strong 5 consistency is sufficient to guarantee global consistency and the continuous endpoint subclass for which strong 3 consistency is sufficient to guarantee global consistency copyright 2001 elsevier science b v

geometric curvature bounds in riemannian manifolds with boundary an alexandrov upper bound on curvature for a riemannian manifold with boundary is proved to be the same as an upper bound on sectional curvature of interior sections and of sections of the boundary which bend away from the interior as corollaries those same sectional curvatures are related to estimates for convexity and conjugate radii the hadamard cartan theorem and yau s isoperimetric inequality for spaces with negative curvature are generalized

a continuous time linear system identification method for slowly sampled data both direct and indirect methods exist for identifying continuous time linear systems a direct method estimates continuous time input and output signals from their samples and then use them to obtain a continuous time model whereas an indirect method estimates a discrete time model first both methods rely on fast sampling to ensure good accuracy in this paper we propose a more direct method where a continuous time linear model is directly fitted to the available samples this method produces an exact model asymptotically modulo some possible aliasing ambiguity even when the sampling rate is relatively slow we also state conditions under which the aliasing ambiguity can be resolved and we provide experiments showing that the proposed method is a valid option when a slow sampling frequency must be used but a large number of samples is available

thick set inversion abstract this paper deals with the set inversion problem x f 1 y in the case where the function f r n r m and the set y are both uncertain the uncertainty is treated under the form of intervals more precisely for all x f x is inside the box f x and the uncertain set y is bracketed between an inner set y and an outer set y the introduction of new tools such as thick intervals and thick boxes will allow us to propose an efficient algorithm that handles the uncertainty of sets in an elegant and efficient manner some elementary test cases that cannot be handled easily and properly by existing methods show the efficiency of the approach

a unified approach to path problems we describe a general method for solving path problems on directed graphs such path problems include finding shortest paths solving sparse systems of linear equations and carrying out global flow analysis of computer programs our method consists of two steps first we construct a collection of regular expressions representing sets of paths in the graph this can be done by using any standard algorithm such as gaussian or gauss jordan elimination next we apply a natural mapping from regular expressions into the given problem domain we exhibit the mappings required to find shortest paths solve sparse systems of linear equations and carry out global flow analysis our results provide a general purpose algorithm for solving any path problem and show that the problem of constructing path expressions is in some sense the most general path problem

on the analytic representation of the leximin ordering and its application to flexible constraint propagation we discuss the basic formulation of constraint propagation problems and extend it to the flexible constraint propagation environment where constraints are represented as fuzzy subsets some methods for ordering alternative solutions with respect to a collection of flexible constraints are discussed along with their drawbacks among the methods introduced is the leximin method where we note its lack of an analytic formulation with the aid of the ordered weighted averaging owa operator we suggest an analytic formulation for the leximin method some properties of this formulation are provided we then describe the application of this new formulation for the leximin method to situations in which the constraints are describable in a linear fashion we show how we can use mixed integer programming techniques to find an optimal solution

tree clustering for constraint networks research note abstract the paper offers a systematic way of regrouping constraints into hierarchical structures capable of supporting search without backtracking the method involves the formation and preprocessing of an acyclic database that permits a large variety of queries and local perturbations to be processed swiftly either by sequential backtrack free procedures or by distributed constraint propagation processes

on the computational complexity of querying bounds on differences constraints abstract given a consistent knowledge base formed by a set of constraints efficient query answering e g checking whether a set of constraints is consistent with the knowledge base or necessarily true in it is practically very important in the paper we consider bounds on differences which are an important class of constraints based on linear inequalities and we analyze the computational complexity of query answering more specifically we consider various common types of queries and we prove that if the minimal network produced by constraint satisfaction algorithms and characterizing the solutions to a set of constraints is maintained then the complexity of answering a query depends only on the dimension of the query and not on the dimension of the knowledge base which is usually much larger than the query we also analyse how the approach can be used to deal efficiently with a class of updates to the knowledge base some applications of the results are sketched in the conclusion

a unifying approach to temporal constraint reasoning abstract we present a formalism disjunctive linear relations dlrs for reasoning about temporal constraints dlrs subsume most of the formalisms for temporal constraint reasoning proposed in the literature and is therefore computationally expensive we also present a restricted type of dlrs horn dlrs which have a polynomial time satisfiability problem we prove that most approaches to tractable temporal constraint reasoning can be encoded as horn dlrs including the ord horn algebra by nebel and burckert and the simple temporal constraints by dechter et al thus dlrs is a suitable unifying formalism for reasoning about temporal constraints

regularized interpolation for noisy images interpolation is the means by which a continuously defined model is fit to discrete data samples when the data samples are exempt of noise it seems desirable to build the model by fitting them exactly in medical imaging where quality is of paramount importance this ideal situation unfortunately does not occur in this paper we propose a scheme that improves on the quality by specifying a tradeoff between fidelity to the data and robustness to the noise we resort to variational principles which allow us to impose smoothness constraints on the model for tackling noisy data based on shift rotation and scale invariant requirements on the model we show that the lp norm of an appropriate vector derivative is the most suitable choice of regularization for this purpose in addition to tikhonov like quadratic regularization this includes edge preserving total variation like tv regularization we give algorithms to recover the continuously defined model from noisy samples and also provide a data driven scheme to determine the optimal amount of regularization we validate our method with numerical examples where we demonstrate its superiority over an exact fit as well as the benefit of tv like nonquadratic regularization over tikhonov like quadratic regularization

temporal constraint satisfaction on causal models abstract causal models play a fundamental role in many intelligent problem solving activities such as diagnosis or simulation although the notion of causation is strictly related to the notion of time temporal knowledge and temporal reasoning have been often disregarded in problem solvers operating on causal models in this page we propose an approach to temporal constraint satisfaction on causal models the approach departs significantly from traditional approaches to temporal constraint satisfaction in our approach in fact we use synchronization mechanisms for dealing with events caused by a conjunction of other events in the paper we analyze the computation complexity of temporal constraint satisfaction on causal models showing that such a form of reasoning is intrinsically intractable i e it is intractable even if one limits the attention to a simple form of causal model and of consistency constraints however we show that it is possible to define sufficient conditions for temporal consistency that can be checked in polynomial time interestingly such conditions allow us to relate our approach to traditional approaches to temporal constraint satisfaction

towards a general theory of action and time abstract a formalism for reasoning about actions is proposed that is based on a temporal logic it allows a much wider range of actions to be described than with previous approaches such as the situation calculus this formalism is then used to characterize the different types of events processes actions and properties that can be described in simple english sentences in addressing this problem we consider actions that involve non activity as well as actions that can only be defined in terms of the beliefs and intentions of the actors finally a framework for planning in a dynamic world with external events and multiple agents is suggested

a unified texture model based on a 2 d wold like decomposition a unified texture model that is applicable to a wide variety of texture types found in natural images is presented this model leads to the derivation of texture analysis and synthesis algorithms designed to estimate the texture parameters and to reconstruct the original texture field from these parameters the texture field is assumed to be a realization of a regular homogeneous random field which is characterized in general by a mixed spectral distribution the texture field is orthogonally decomposed into a purely indeterministic component and a deterministic component the deterministic component is further orthogonally decomposed into a harmonic component and a generalized evanescent component both analytical and experimental results show that the deterministic components should be parameterized separately from the purely indeterministic component the model is very efficient in terms of the number of parameters required to faithfully represent textures reconstructed textures are practically indistinguishable from the originals

on point duration networks for temporal reasoning we present here a point duration network formalism which extends the point algebra model to include additional variables that represent durations between points of time thereafter the new qualitative model is enlarged for allowing unary metric constraints on points and durations subsuming in this way several point based approaches to temporal reasoning we deal with some reasoning tasks within the new models and we show that the main problem deciding consistency is np complete however tractable special cases are identified and we show efficient algorithms for checking consistency finding a solution and obtaining the minimal network

a unified formulation of gaussian versus sparse stochastic processes part ii discrete domain theory this paper is devoted to the characterization of an extended family of continuous time autoregressive moving average carma processes that are solutions of stochastic differential equations driven by white levy innovations these are completely specified by 1 a set of poles and zeros that fixes their correlation structure and 2 a canonical infinitely divisible probability distribution that controls their degree of sparsity with the gaussian model corresponding to the least sparse scenario the generalized carma processes are either stationary or nonstationary depending on the location of the poles in the complex plane the most basic nonstationary representatives with a single pole at the origin are the levy processes which are the non gaussian counterparts of brownian motion we focus on the general analog to discrete conversion problem and introduce a novel spline based formalism that greatly simplifies the derivation of the correlation properties and joint probability distributions of the discrete versions of these processes we also rely on the concept of generalized increment process which suppresses all long range dependencies to specify an equivalent discrete domain innovation model a crucial ingredient is the existence of a minimally supported function associated with the whitening operator l this b spline which is fundamental to our formulation appears in most of our formulas both at the level of the correlation and the characteristic function we make use of these discrete domain results to numerically generate illustrative examples of sparse signals that are consistent with the continuous domain model

constraint propagation with interval labels abstract constraint propagation is often used in ai systems to perform inference about quantities this paper studies one particular kind of constraint propagation where quantities are labelled with signs or with intervals and these labels are propagated through recorded constraints we review the uses of such inference schemes in ai systems of various kinds and evaluate their strengths and weaknesses in particular we determine the completeness and running time of constraint propagation for various kinds of labels and constraints

accelerating filtering techniques for numeric csps search algorithms for solving numeric csps constraint satisfaction problems make an extensive use of filtering techniques in this paper we show how those filtering techniques can be accelerated by discovering and exploiting some regularities during the filtering process two kinds of regularities are discussed cyclic phenomena in the propagation queue and numeric regularities of the domains of the variables we also present in this paper an attempt to unify numeric csps solving methods from two distinct communities that of csp in artificial intelligence and that of interval analysis

estimation of continuous time ar process parameters from discrete time data the problem of estimating continuous time autoregressive process parameters from discrete time data is considered the basic approach used here is based on replacing the derivatives in the model by discrete time differences forming a linear regression and using the least squares method such a procedure is simple to apply computationally flexible and efficient and may have good numerical properties it is known however that all standard approximations of the highest order derivative such as repeated use of the delta operator gives a biased least squares estimate even as the sampling interval tends to zero some of our previous approaches to overcome this problem are reviewed then two new methods which avoid the shift in our previous results are presented one of them which is termed bias compensation is computationally very efficient finally the relationship of the above least squares approaches with an instrumental variable method is investigated comparative simulation results are also presented

planning routes through uncertain territory abstract planning routes and executing them requires both topological and metric information a natural implementation of a cognitive map might therefore consist of an assertional data base for topological information and a fuzzy map for the metric information a fuzzy map captures facts about objects by recording their relative positions orientations and scales in convenient frames of reference it is fuzzy in the sense that coordinates are specified to lie in a range rather than having fixed values the fuzzy map allows easy retrieval of information the same information is also represented in a discrimination tree which allows an object to be retrieved given its location and other attributes the problem of constructing a fuzzy map is more difficult we present a partial solution an algorithm that assimilates a fact first by imposing constraints on the fuzzy coordinates of the objects involved then by rearranging or growing the tree of frames of reference route planning is modelled as a process of finding the overall direction and topology of the path then filling in the details by deciding how to go around barriers it uses the retrieval algorithms our program spam carries out all these processes

guaranteed nonlinear estimation using constraint propagation on sets bounded error estimation is the estimation of the parameter or state vector of a model from experimental data under the assumption that some suitably de ned errors should belong to some prior feasible sets when the model outputs are linear in the vector to be estimated a number of methods are available to enclose all estimates that are consistent with the data into simple sets such as ellipsoids orthotopes or parallelotopes thereby providing guaranteed set estimates in the nonlinear case the situation is much less developed and there are very few methods that produce such guaranteed estimates in this paper the discrete time problem is cast into the more general framework of constraint satisfaction problems algorithms rather classical in the area of interval constraint propagation are extended by replacing intervals by more general subsets of real vector spaces this makes it possible to propose a new algorithm that contracts the feasible domains for each uncertain variable optimally i e no smaller domain could be obtained and e ciently the resulting methodology is illustrated on discrete time nonlinear state estimation the state at time k is estimated either from past measurement only or from all measurements assumed to be available from the start even in the causal case prior information on the future value of the state and output vectors due for instance to physical constraints is readily taken into account

estimation of continuous time autoregressive model from finely sampled data we extend our two earlier continuous time estimation methods for continuous time autoregressive car model to derive estimators using only finely sampled discrete time data the approach is based on the approximation of derivatives by divided differences coupled with some bias correction two types of estimators are provided having bias of the order o h or of o h sup 2 respectively for small sampling interval h the procedures are computationally efficient and always yield a stable autoregressive polynomial simulations show that their bias are quite low

technical communique nonlinear bounded error state estimation of continuous time systems this paper presents a first study on the application of interval analysis and consistency techniques to state estimation of continuous time systems described by nonlinear ordinary differential equations the approach is presented in a bounded error context and the resulting methodology is illustrated by an example

fuzzy constraint networks for signal pattern recognition this paper deals with representation and reasoning on information concerning the evolution of a physical parameter by means of a model based on the fuzzy constraint satisfaction problem formalism and with which it is possible to define what we call fuzzy temporal profiles ftp based on fundamentally linguistic information this model allows the integration of knowledge on the evolution of a set of parameters into a knowledge representation scheme in which time plays a fundamental role the ftp model describes the behaviour of a physical parameter on the basis of a set of signal events and which allows the evolution of the parameter between each pair of events to be modelled as signal episodes given the fundamentally linguistic nature of the information represented the consistency analysis of this information is an essential task nevertheless the obtention of the minimal representation of the network that defines an ftp is an np hard problem in spite of this we supply algorithms guaranteeing local levels of consistency that allow to correct a large proportion of the errors committed by a human expert in the linguistic description of the profile furthermore we propose a new topology whose consistency can be guaranteed in polynomial time we also study the applicability of this model in the recognition of signal patterns