nonlinear analysis of river flow time sequences within the field of chaos theory several methods for the analysis of complex dynamical systems have recently been proposed in light of these ideas we study the dynamics which control the behavior over time of river flow investigating the existence of a low dimension deterministic component the present article follows the research undertaken in the work of porporato and ridolfi 1996a in which some clues as to the existence of chaos were collected particular emphasis is given here to the problem of noise and to nonlinear prediction with regard to the latter the benefits obtainable by means of the interpolation of the available time series are reported and the remarkable predictive results attained with this nonlinear method are shown

optimal observation network design for parameter structure identification in groundwater modeling 1 designing a pumping observation experiment for parameter identification in groundwater modeling is a very challenging problem because the parameter structure such as the hydraulic conductivity of a heterogeneous aquifer is usually unknown generally to identify a more complex parameter structure requires more observation data traditional experimental design methods have two disadvantages first the sufficiency of information provided by a design is not considered in the design stage second the decision variables often are dependent on the unknown parameter because of the nonlinearity of the model as a result a sequential design observation process should be used but this often is impractical in groundwater modeling this paper presents a new methodology for experimental design that can find a minimum cost design to provide sufficient information for identifying both the parameter structure and parameter values sequential gaussian simulation is used to generate different realizations for the unknown parameter for each possible realization the number of observation wells is increased gradually during the design process until the information provided by the design is sufficient the selection criterion for locating a new observation well is the maximization of the information content for parameter identification the overall sufficiency of a design is assessed by monte carlo simulation the proposed methodology is explained in detail by a numerical example

nonlinear black box modeling in system identification a unified overview a nonlinear black box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics there has been considerable recent interest in this area with structures based on neural networks radial basis networks wavelet networks and hinging hyperplanes as well as wavelet transform based methods and models based on fuzzy sets and fuzzy rules this paper describes all these approaches in a common framework from a user s perspective it focuses on what are the common features in the different approaches the choices that have to be made and what considerations are relevant for a successful system identification application of these techniques it is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space these mappings are discussed separately the latter mapping is usually formed as a basis function expansion the basis functions are typically formed from one simple scalar function which is modified in terms of scale and location the expansion from the scalar argument to the regressor space is achieved by a radial or a ridge type approach basic techniques for estimating the parameters in the structures are criterion minimization as well as two step procedures where first the relevant basis functions are determined using data and then a linear least squares step to determine the coordinates of the function approximation a particular problem is to deal with the large number of potentially necessary parameters this is handled by making the number of used parameters considerably less than the number of offered parameters by regularization shrinking pruning or regressor selection

varying coefficient models we explore a class of regression and generalized regression models in which the coefficients are allowed to vary as smooth functions of other variables general algorithms are presented for estimating the models flexibly and some examples are given this class of models ties together generalized additive models and dynamic generalized linear models into one common framework when applied to the proportional hazards model for survival data this approach provides a new way of modelling departures from the proportional hazards assumption

towards a coherent philosophy for modelling the environment the predominant philosophy underlying most environmental modelling is a form of pragmatic realism the limitations of this approach in practical applications are discussed in particular in relati

identification of non linear stochastic systems by state dependent parameter estimation the paper outlines how improved estimates of time variable parameters in models of stochastic dynamic systems can be obtained using recursive filtering and fixed interval smoothing techniques with the associated hyper parameters optimized by maximum likelihood based on prediction error decomposition it then shows how by exploiting special data re ordering and back fitting procedures similar recursive parameter estimation techniques can be utilized to estimate much more rapid state dependent parameter sdp variations in this manner it is possible to identify and estimate a widely applicable class of nonlinear stochastic systems as illustrated by several examples that include simulated and real data from chaotic processes finally the paper points out that such sdp models can form the basis for new methods of signal processing automatic control and state estimation for nonlinear stochastic systems

an extensional treatment of lazy data flow deadlock in an extensional treatment of dataflow deadlock wa81 wadge introduced an elegant non operational test for proving that many of kahn s data flow message passing networks ka74 must be free of deadlock a test that should extend to a much wider context in the study of program correctness such a context has now been provided with the introduction of partial metric spaces ma92 these spaces can be used to describe semantic domains such as those used in lazy data flow languages w a85 this paper develops wadge s ideas on establishing an extensional theory of program correctness by using partial metric spaces to give a non operational treatment of lazy data flow deadlock

attack vulnerability of complex networks we study the response of complex networks subject to attacks on vertices and edges several existing complex network models as well as real world networks of scientific collaborations and internet traffic are numerically investigated and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph for each case of attacks on vertices and edges four different attacking strategies are used removals by the descending order of the degree and the betweenness centrality calculated for either the initial network or the current network during the removal procedure it is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network suggesting that the network structure changes as important vertices or edges are removed furthermore the correlation between the betweenness centrality and the degree in complex networks is studied

identifying the influential nodes via eigen centrality from the differences and similarities of structure abstract one of the most important problems in complex network is the identification of the influential nodes for this purpose the use of differences and similarities of structure to enrich the centrality method in complex networks is proposed the centrality method called ecds centrality used is the eigen centrality which is based on the jaccard similarities between the two random nodes this can be described by an eigenvalues problem here we use a tunable parameter to adjust the influence of the differences and similarities comparing with the results of the susceptible infected recovered sir model for four real networks the ecds centrality could identify influential nodes more accurately than the tradition centralities such as the k shell degree and closeness centralities especially in the erdos network the kendall s tau could be reached to 0 93 when the spreading rate is 0 12 in the us airline network the kendall s tau could be reached to 0 95 when the spreading rate is 0 06

nonparametric direct mapping of rainfall runoff relationships an alternative approach to data analysis and modeling we present a new approach for the analysis and modeling of catchment rainfall runoff relationships that uses as predictor variables input history summary variables only the latter are defined as linear combinations of inputs at a given number of previous time steps this transforms the dynamic identification problem into a static one as the identification algorithm we use regression trees which act as a nonlinear nonparametric model the original algorithm is adapted to account for serial correlation in variables the new method is applied to two subcatchments of the u s department of agriculture forest service andrews experimental forest watershed oregon united states simple and interpretable tree models explain more than 80 of the initial deviance of the observations in both calibration and validation this suggests that the selected variables have a good predictive power and that further modeling attempts using them are warranted the models show a distinct pattern of the selected explanatory variables applications of the method include data quality control comparative analysis assessment of hydrological change and multicriterion evaluation of parametric hydrological models

comments on identification of non linear parametrically varying models using separable least squares by f previdi and m lovera black box or open box this note compares and contrasts the non linear parameter varying nlpv and state dependent parameter sdp model classes it shows that while they have similarities the two stage sdp modelling procedure involving non parametric identification followed by parametric estimation is quite different from the single stage nlpv procedure in particular the sdp procedure allows for the identification of the model structure and the nature of the non linearities prior to the estimation of the parameters that characterize this identified model structure in contrast to nlpv modelling therefore sdp estimation opens up the black box and reveals the inner nature of the non linear system

sliding mode scheme for adaptive specific growth rate control in biotechnological fed batch processes this paper addresses the control of biomass growth rate in fed batch bioreactors the main difficulty when designing controllers for these processes is the lack of accurate on line knowledge of the controlled variable as well as the strong parameter and model uncertainties a completely novel approach to the control design is introduced in this paper which allows us to overcome these problems in fact the proposed controller which is applicable to a large class of fermentation processes requires minimal knowledge of the process parameters and only uses on line measurement of volume and biomass concentration first a reference model is proposed and a goal manifold in the state space is derived where the control objective is satisfied a partial state feedback law is then proved to be an invariant control for the goal manifold then the feedback gain is dynamically adjusted via a discontinuous action that enforces a sliding regime such that all state trajectories are steered towards the goal manifold

cpo semantics of timed interactive actor networks we give a denotational framework for composing interactive components into closed or open systems and show how to adapt classical domain theoretic approaches to open systems and to timed systems for timed systems prior approaches are based on temporal logics automata theory or metric spaces in this paper we base the semantics on a cpo with a prefix order as has been done previously for untimed systems we show that existence and uniqueness of behaviors are ensured by continuity with respect to this prefix order existence and uniqueness of behaviors however do not imply that a composition of components yields a useful behavior the unique behavior could be empty or smaller than expected we define liveness and show that appropriately defined causality conditions ensure liveness and freedom from zeno conditions in our formulation causality does not require a metric and can embrace a wide variety of models of time

estimation and validation of contemporaneous parma models for streamflow simulation seasonal streamflow series generally exhibit periodicity in the autocovariance structure such periodicity can be represented by parma models i e autoregressive moving average arma models with parameters that vary with the seasons statistical properties of low order models such as the parma 2 2 model are examined the periodic moment equations are derived they can be used to compute the periodic covariance structure of a given model simulation of streamflow at several sites can be done using the contemporaneous parma model the main problem in using such models is to determine the covariance matrices of innovations traditionally this has been done by the method of maximum likelihood however this method generally leads to significant underestimation of the cross correlation of flows a moment estimator is developed herein for the contemporaneous parma 2 2 model along with three approximate moment based estimators for those cases where a feasible moment solution cannot be obtained the applicability of the proposed methods is illustrated by fitting parma models to weekly flow data for two catchments in the ottawa river basin

on quasi metric aggregation functions and fixed point theorems the problem of how to merge by means of a function a family of metrics into a single one was studied deeply by j borsik and j dobos on a product of metric spaces math slovaca31 1981 193 205 motivated by the utility of quasi metrics in computer science the borsik and dobos study was extended to the quasi metric context in such a way that a general description of how to combine through a function a family of quasi metrics in order to obtain a single one as output was provided by g mayor and o valero aggregation of asymmetric distances in computer science inform sci 180 2010 803 812 in this paper inspired by the fact that fixed point theory provides an efficient tool in many fields of applied sciences we have proved fixed point theorems for a new type of contractions that we have called projective f contractions defined between quasi metric spaces that have been obtained via the so called quasi metric aggregation functions moreover we show that the new fixed point results are useful to discuss on the one hand the complexity of a collection of recursive programs whose running times of computing hold a coupled system of recurrence equations and on the other hand to analyze simultaneously the complexity and the correctness of recursive algorithms that perform a computation by means of a recursive denotational specification

stratified negation in temporal logic programming and the cycle sum test we consider stratified negation in temporal logic programming we demonstrate that the cycle sum test which was initially proposed for detecting deadlocks in the context of temporal functional programming can also be used as a syntactic stratification test for temporal logic programming therefore on the one hand we exhibit a class of temporal logic programs with negation which have a well defined semantics and on the other hand we provide further evidence that the cycle sum test is a fundamental one in the area of temporal programming

robustness of weighted networks abstract complex network response to node loss is a central question in different fields of network science because node failure can cause the fragmentation of the network thus compromising the system functioning previous studies considered binary networks where the intensity weight of the links is not accounted for i e a link is either present or absent however in real world networks the weights of connections and thus their importance for network functioning can be widely different here we analyzed the response of real world and model networks to node loss accounting for link intensity and the weighted structure of the network we used both classic binary node properties and network functioning measure introduced a weighted rank for node importance node strength and used a measure for network functioning that accounts for the weight of the links weighted efficiency we find that i the efficiency of the attack strategies changed using binary or weighted network functioning measures both for real world or model networks ii in some cases removing nodes according to weighted rank produced the highest damage when functioning was measured by the weighted efficiency iii adopting weighted measure for the network damage changed the efficacy of the attack strategy with respect the binary analyses our results show that if the weighted structure of complex networks is not taken into account this may produce misleading models to forecast the system response to node failure i e consider binary links may not unveil the real damage induced in the system last once weighted measures are introduced in order to discover the best attack strategy it is important to analyze the network response to node loss using nodes rank accounting the intensity of the links to the node

locally weighted regression an approach to regression analysis by local fitting abstract locally weighted regression or loess is a way of estimating a regression surface through a multivariate smoothing procedure fitting a function of the independent variables locally and in a moving fashion analogous to how a moving average is computed for a time series with local fitting we can estimate a much wider class of regression surfaces than with the usual classes of parametric functions such as polynomials the goal of this article is to show through applications how loess can be used for three purposes data exploration diagnostic checking of parametric models and providing a nonparametric regression surface along the way the following methodology is introduced a a multivariate smoothing procedure that is an extension of univariate locally weighted regression b statistical procedures that are analogous to those used in the least squares fitting of parametric functions c several graphical methods that are useful tools for understanding loess estimates and checking the a

robustness of network of networks under targeted attack the robustness of a network of networks non under random attack has been studied recently gao et al phys rev lett 107 195701 2011 understanding how robust a non is to targeted attacks is a major challenge when designing resilient infrastructures we address here the question how the robustness of a non is affected by targeted attack on high or low degree nodes we introduce a targeted attack probability function that is dependent upon node degree and study the robustness of two types of non under targeted attack i a tree of n fully interdependent erd s renyi or scale free networks and ii a starlike network of n partially interdependent erd s renyi networks for any tree of n fully interdependent erd s renyi networks and scale free networks under targeted attack we find that the network becomes significantly more vulnerable when nodes of higher degree have higher probability to fail when the probability that a node will fail is proportional to its degree for a non composed of erd s renyi networks we find analytical solutions for the mutual giant component p as a function of p where 1 p is the initial fraction of failed nodes in each network we also find analytical solutions for the critical fraction p c which causes the fragmentation of the n interdependent networks and for the minimum average degree k over min below which the non will collapse even if only a single node fails for a starlike non of n partially interdependent erd s renyi networks under targeted attack we find the critical coupling strength q c for different n when q q c the attacked system undergoes an abrupt first order type transition when q q c the system displays a smooth second order percolation transition we also evaluate how the central network becomes more vulnerable as the number of networks with the same coupling strength q increases the limit of q 0 represents no dependency and the results are consistent with the classical percolation theory of a single network under targeted attack

the order theoretic duality and relations between partial metrics and local equalities for a gl monoid l hohle introduced l valued sets to formalize the mathematical theory of identity and existence in monoidal logic for the purpose of analyzing deadlock situations in data flow networks the notion of partial metric space was proposed by matthews later on a lattice theoretic generalization of pseudo partial metric spaces extending the range of pseudo partial metrics to a value lattice v were studied under the name of v pseudo pmetric spaces referring to a gl monoid l and a dual gl monoid v we show that l valued sets are order theoretically dual to v pseudopmetric spaces and apply this duality to the representation of v pseudopmetric spaces and the determination of their various categorical properties in addition to this the present paper provides not only some new results about the representation of l valued sets and their categories but also some non trivial categorical connections between l valued sets and v pseudopmetric spaces

identification of non linear parametrically varying models using separable least squares in this paper a novel identification algorithm for a class of non linear possibly parameter varying models is proposed the algorithm is based on separable least squares ideas these models are given in the form of a linear fractional transformation lft where the forward part is represented by a conventional linear regression and the feedback part is given by a non linear map which can take into account scheduling variables available for measurement the non linear part of the model can be parameterized according to various paradigms like e g neural network nn or general nonlinear autoregressive exogenous narx models the estimation algorithm exploits the separability of the criterion used to estimate the parameters when using a nn it is possible the explicit computation of the frechet derivative needed to implement a separable least square algorithm

nearest neighbor methods for nonparametric rainfall runoff forecasting this paper introduces a nonparametric regression method for time series to the stochastic hydrology literature the technique known as the nearest neighbor nn method is motivated by the classical rainfall runoff forecasting problem we have shown in a companion paper yakowitz 1987 that in the time series setting it has attractive large sample properties as summarized herein these include asymptotic convergence to the optimal forecaster under many circumstances in which the forecaster is not a linear function of the data the technique is applied to a sequence of about 3000 seasonalized rainfall runoff pairs from the coshocton watershed we compare the nn forecasting performance on a split sample with those of autoregressive maximum and instantaneous unit hydrograph forecasters

an iterative growing and pruning algorithm for classification tree design a critical issue in classification tree design obtaining right sized trees i e trees which neither underfit nor overfit the data is addressed instead of stopping rules to halt partitioning the approach of growing a large tree with pure terminal nodes and selectively pruning it back is used a new efficient iterative method is proposed to grow and prune classification trees this method divides the data sample into two subsets and iteratively grows a tree with one subset and prunes it with the other subset successively interchanging the roles of the two subsets the convergence and other properties of the algorithm are established theoretical and practical considerations suggest that the iterative free growing and pruning algorithm should perform better and require less computation than other widely used tree growing and pruning algorithms numerical results on a waveform recognition problem are presented to support this view

conditional parametric models for storm sewer runoff 1 the method of conditional parametric modeling is introduced for flow prediction in a sewage system it is a well known fact that in hydrological modeling the response runoff to input precipitation varies depending on soil moisture and several other factors consequently nonlinear input output models are needed the model formulation described in this paper is similar to the traditional linear models like final impulse response fir and autoregressive exogenous arx except that the parameters vary as a function of some external variables the parameter variation is modeled by local lines using kernels for local linear regression as such the method might be referred to as a nearest neighbor method the results achieved in this study were compared to results from the conventional linear methods fir and arx the increase in the coefficient of determination is substantial furthermore the new approach conserves the mass balance better hence this new approach looks promising for various hydrological models and analysis

the correspondence between partial metrics and semivaluations partial metrics or the equivalent weightable quasi metrics have been introduced in matthews proc 8th summer conf on general topology and applications ann new york acad sci 728 1994 183 as part of the study of the denotational semantics of data flow networks theoret comput sci 151 1995 195 the interest in valuations in connection to domain theory derives from e g jones and plotkin lics 89 ieee computer society press silver spring md 1998 pp 186 195 jones ph d thesis university of edinburgh 1989 edalat lics 94 ieee computer society press silver spring md 1994 and heckmann fund inform 24 3 1995 259 connections between partial metrics and valuations have been discussed in the literature e g o neill in s andima et al eds proc 11th summer conf on general topology and applications ann new york acad sci 806 1997 304 bukatin and scott in s adian a nerode eds logical foundations of computer science lecture notes in computer science vol 1234 springer berlin 1997 pp 33 43 and bukatin and shorina in m nivat ed foundations of software science and computation structures lecture notes in computer science vol 1378 springer berlin 1998 pp 125 139 in each case partial metrics are generated from strictly increasing valuations we analyze the precise relationship between these two notions it is well known that characterizations of partial metrics in general are hard to obtain as witnessed by the open characterization problems in the survey paper nonsymmetric topology kunzi bolyai soc math stud 4 1993 303 our approach to obtaining such a characterization involves the isolation of a mathematically nice class of spaces which is sufficiently large to incorporate the quantitative domain theoretic examples involving partial metric spaces for these purposes we focus on the class of quasi metric semilattices these structures as will be illustrated arise naturally in quantitative domain theory and include in particular the class of totally bounded scott domains discussed in smyth in g m reed a w roscoe r f wachter ed topology and category theory in computer science oxford university press oxford 1991 pp 207 229 the baire quasi metric spaces of theoret comput sci 151 1995 195 the complexity spaces of schellekens in proc mfps 11 electronic notes in theoretical computer science vol i elsevier amsterdam 1995 pp 211 232 and the interval domain proc twelfth ann ieee symp on logic in computer science ieee press new york 1997 pp 248 257 we introduce the notion of a semivaluation which generalizes the fruitful notion of a valuation on a lattice to the context of semilattices and establish a correspondence between partial metric semilattices and semivaluation spaces

efficiency of attack strategies on complex model and real world networks we investigated the efficiency of attack strategies to network nodes when targeting several complex model and real world networks we tested 5 attack strategies 3 of which were introduced in this work for the first time to attack 3 model networks erdos and renyi barabasi and albert preferential attachment network and scale free network configuration models and 3 real networks gnutella peer to peer network email network of the university of rovira i virgili and immunoglobulin interaction network nodes were removed sequentially according to the importance criterion defined by the attack strategy and we used the size of the largest connected component lcc as a measure of network damage we found that the efficiency of attack strategies fraction of nodes to be deleted for a given reduction of lcc size depends on the topology of the network although attacks based on either the number of connections of a node or betweenness centrality were often the most efficient strategies sequential deletion of nodes in decreasing order of betweenness centrality was the most efficient attack strategy when targeting real world networks the relative efficiency of attack strategies often changed during the sequential removal of nodes especially for networks with power law degree distribution

a characterization of partial metrizability a characterization of partial metrizability is given which provides a partial solution to an open problem stated by kunzi in the survey paper non symmetric topology in proceedings of the szekszard conference bolyai soc math studies vol 4 1993 pp 303 338 problem 711 characterize those quasi uniformities having a countable base which are induced by a weighted quasi metric the characterization yields a powerful tool which establishes a correspondence between partial metrics and special types of valuations referred to as q valuations cf also theoret comput sci to appear the notion of a q valuation essentially combines the well known notion of a valuation with a weaker version of the notion of a quasi unimorphism i e an isomorphism in the context of quasi uniform spaces as an application we show that continuous directed complete partial orders dcpos are quantifiable in the sense of o neill in s andima et al eds proceedings of the 11th summer conference on general topology and applications annals of the new york academy of sciences vol 86 1997 pp 304 315 i e the scott topology and partial order are induced by a partial metric for algebraic dcpos the lawson topology is induced by the associated metric the partial metrization of general domains improves prior approaches in two ways the partial metric is guaranteed to capture the scott topology as opposed to e g smyth quasi uniformities reconciling domains with metric spaces lecture notes in computer science vol 298 springer berlin 1987 pp 236 253 bonsangue et al theoret comput sci 193 1998 1 flagg theoret comput sci to appear and flagg theoret comput sci 177 1 1997 1 which in general yield a coarser topology partial metric spaces are smyth completable and hence their smyth completion reduces to the standard bicompletion this type of simplification is advocated in smyth in g m reed a w roscoe r f wachter eds topology and category theory in computer science oxford university press oxford 1991 pp 207 229 our results extend smyth 1991 s scope of application from the context of 2 3 sfp domains to general domains the quantification of general domains solves an open problem on the partial metrizability of domains22pawel waszkiewicz communicated recently to the author that he obtained similar results independently a stronger result implying the quantifiability of general domains is reported in 23 o neill obtained the partial metrizability of algebraic domains in his thesis 29 stated in o neil 1997 and heckmann appl categor struct 1999 71 our proof of the quantifiability of domains is novel in that it relies on the central notion of a semivaluation schellekens the correspondence between partial metrics and semivaluations theoret comput sci to appear the characterization of partial metrizability is entirely new and sheds light on the deeper connections between partial metrics and valuations commented on in bukatin and shorina in m nivat ed foundations of software science and computation structures lecture notes in computer science vol 1378 springer berlin 1998 pp 125 139 based on schellekens the correspondence between partial metrics and semivaluations theoret comput sci to appear and our present characterization we conclude that the notion of a semi valuation is central in the context of quantitative domain theory since it can be shown to underlie the various models arising in the applications

new attack strategies for complex networks abstract the invulnerability of complex networks is an important issue in that the behavior of scale free network differs from that of exponential network according to the structural characteristics of the networks we propose two new attack strategies named idb initial degree and betweenness and rdb recalculated degree and betweenness the strategies are originated from id initial degree distribution and rd recalculated degree distribution strategies in which attacks are based on initial structural information of a network the probability of node removals depends on a new metric combining degree centrality and betweenness centrality we evaluate the efficiency of the proposed strategies on one real world network and three network models experimental results indicate that the proposed strategies are more efficient than the traditional id and rd strategies specially the ws small world network behaves more sensitive to the proposed strategies the attack efficiency of rdb strategy is improved by 20 to rd strategy and idb strategy is improved by 40 to id strategy