interval process model and non random vibration analysis abstract this paper develops an interval process model for time varying or dynamic uncertainty analysis when information of the uncertain parameter is inadequate by using the interval process model to describe a time varying uncertain parameter only its upper and lower bounds are required at each time point rather than its precise probability distribution which is quite different from the traditional stochastic process model a correlation function is defined for quantification of correlation between the uncertain but bounded variables at different times and a matrix decomposition based method is presented to transform the original dependent interval process into an independent one for convenience of subsequent uncertainty analysis more importantly based on the interval process model a non random vibration analysis method is proposed for response computation of structures subjected to time varying uncertain external excitations or loads the structural dynamic responses thus can be derived in the form of upper and lower bounds providing an important guidance for practical safety analysis and reliability design of structures finally two numerical examples and one engineering application are investigated to demonstrate the feasibility of the interval process model and corresponding non random vibration analysis method

kernelized multiview projection for robust action recognition conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features in this paper we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called kernelized multiview projection kmp computing the kernel matrices from different features views via time sequential distance learning kmp can encode different features with different weights to achieve a low dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative more crucially kmp is linear for the reproducing kernel hilbert space which allows it to be competent for various practical applications we demonstrate kmp s performance for action recognition on five popular action datasets and the results are consistently superior to state of the art techniques

a generalized goodwin business cycle model in random environment abstract this work develops a generalization of the goodwin lotka volterra models the traditional model describes the evolution of the employment rate and the labor share motivated by recent developments on business cycles and goodwin type models we consider a generalized goodwin business cycle model with noise we aim at investigating the impact of telegraph noise and white noise on the evolution of trajectories and the energy on trajectories of the model it is proved that with the presence of the noises all trajectories exit any compact subset of the open unit square d 0 1 0 1 and the energy along trajectories can be arbitrarily large moreover under certain additional conditions the boundary of d is the stable limit cycle of the perturbed model by white noise noise impact on the period of business cycles is also discussed numerical examples are provided to demonstrate our findings

a survey on learning to hash nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest learning to hash is one of the major solutions to this problem and has been widely studied recently in this paper we present a comprehensive survey of the learning to hash algorithms categorize them according to the manners of preserving the similarities into pairwise similarity preserving multiwise similarity preserving implicit similarity preserving as well as quantization and discuss their relations we separate quantization from pairwise similarity preserving as the objective function is very different though quantization as we show can be derived from preserving the pairwise similarities in addition we present the evaluation protocols and the general performance analysis and point out that the quantization algorithms perform superiorly in terms of search accuracy search time cost and space cost finally we introduce a few emerging topics

a nonparametric probabilistic approach for quantifying uncertainties in low dimensional and high dimensional nonlinear models summary a nonparametric probabilistic approach for modeling uncertainties in projection based nonlinear reduced order models is presented when experimental data are available this approach can also quantify uncertainties in the associated high dimensional models the main underlying idea is twofold first to substitute the deterministic reduced order basis rob with a stochastic counterpart second to construct the probability measure of the stochastic reduced order basis srob on a subset of a compact stiefel manifold in order to preserve some important properties of a rob the stochastic modeling is performed so that the probability distribution of the constructed srob depends on a small number of hyperparameters these are determined by solving a reduced order statistical inverse problem the mathematical properties of this novel approach for quantifying model uncertainties are analyzed through theoretical developments and numerical simulations its potential is demonstrated through several example problems from computational structural dynamics copyright 2016 john wiley sons ltd

interval uncertain method for multibody mechanical systems using chebyshev inclusion functions this study proposes a new uncertain analysis method for multibody dynamics of mechanical systems based on chebyshev inclusion functions the interval model accounts for the uncertainties in multibody mechanical systems comprising uncertain but bounded parameters which only requires lower and upper bounds of uncertain parameters without having to know probability distributions a chebyshev inclusion function based on the truncated chebyshev series rather than the taylor inclusion function is proposed to achieve sharper and tighter bounds for meaningful solutions of interval functions to effectively handle the overestimation caused by the wrapping effect intrinsic to interval computations the mehler integral is used to evaluate the coefficients of chebyshev polynomials in the numerical implementation the multibody dynamics of mechanical systems are governed by index 3 differential algebraic equations daes including a combination of differential equations and algebraic equations responsible for the dynamics of the system subject to certain constraints the proposed interval method with chebyshev inclusion functions is applied to solve the daes in association with appropriate numerical solvers this study employs hht i3 as the numerical solver to transform the daes into a series of nonlinear algebraic equations at each integration time step which are solved further by using the newton raphson iterative method at the current time step two typical multibody dynamic systems with interval parameters the slider crank and double pendulum mechanisms are employed to demonstrate the effectiveness of the proposed methodology the results show that the proposed methodology can supply sufficient numerical accuracy with a reasonable computational cost and is able to effectively handle the wrapping effect as cosine functions are incorporated to sharpen the range of non monotonic interval functions copyright 2013 john wiley sons ltd

a novel interval finite element method based on the improved interval analysis abstract static analysis of linear elastic structures with uncertain parameters subjected to deterministic loads is addressed the uncertain structural properties are modeled as interval variables with assigned lower bound and upper bound a novel interval finite element method is formulated in the framework of the improved interval analysis via extra unitary interval recently proposed to limit the conservatism affecting the classical interval analysis the key idea of the novel method is to associate an extra unitary interval to each uncertain parameter in order to keep physical properties linked to the finite elements in both the assembly and solution phases this allows one to reduce overestimation and perform standard assembly of the interval element matrices the lower bound and upper bound of interval displacements and stresses are evaluated by applying two different strategies both based on the so called interval rational series expansion for deriving the approximate explicit inverse of the interval global stiffness matrix numerical examples concerning 2d and 3d structures with uncertain young s modulus are presented to demonstrate the accuracy and efficiency of the proposed procedure

identification and quantification of multivariate interval uncertainty in finite element models abstract the objective of this work is to develop and validate a methodology for the identification and quantification of multivariate interval uncertainty in finite element models the principal idea is to find a solution to an inverse problem where the variability on the output side of the model is known from measurement data but the multivariate uncertainty on the input parameters is unknown for this purpose the uncertain simulation results set created by propagating interval uncertainty through the model is represented by its convex hull the same concept is used to model the uncertainty in the measurements a metric to describe the discrepancy between these convex hulls is defined based on the difference between their volumes and their mutual intersection by minimisation of this metric the interval uncertainty on the input side of the model is identified it is further shown how the procedure can be optimised with respect to output quantity selection validation of the methodology is done using simulated measurement data in two case studies numerically exact identification of multiple coupled parameters having interval uncertainty is possible following the proposed methodology furthermore the robustness of the method with respect to the analyst s initial estimate of the input uncertainty is illustrated the method presented in this work in se is generic but for the examples in this paper it is specifically applied to dynamic models using eigenfrequencies as output quantities as commonly applied in modal updating procedures

multiview alignment hashing for efficient image search hashing is a popular and efficient method for nearest neighbor search in large scale data spaces by embedding high dimensional feature descriptors into a similarity preserving hamming space with a low dimension for most hashing methods the performance of retrieval heavily depends on the choice of the high dimensional feature descriptor furthermore a single type of feature cannot be descriptive enough for different images when it is used for hashing thus how to combine multiple representations for learning effective hashing functions is an imminent task in this paper we present a novel unsupervised multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization which can find a compact representation uncovering the hidden semantics and simultaneously respecting the joint probability distribution of data in particular we aim to seek a matrix factorization to effectively fuse the multiple information sources meanwhile discarding the feature redundancy since the raised problem is regarded as nonconvex and discrete our objective function is then optimized via an alternate way with relaxation and converges to a locally optimal solution after finding the low dimensional representation the hashing functions are finally obtained through multivariable logistic regression the proposed method is systematically evaluated on three data sets 1 caltech 256 2 cifar 10 and 3 cifar 20 and the results show that our method significantly outperforms the state of the art multiview hashing techniques

the stochastic finite element method past present and future a powerful tool in computational stochastic mechanics is the stochastic finite element method sfem sfem is an extension of the classical deterministic fe approach to the stochastic framework i e to the solution of static and dynamic problems with stochastic mechanical geometric and or loading properties the considerable attention that sfem received over the last decade can be mainly attributed to the spectacular growth of computing power rendering possible the efficient treatment of large scale problems this article aims at providing a state of the art review of past and recent developments in the sfem area and indicating future directions as well as some open issues to be examined by the computational mechanics community in the future

sequential compact code learning for unsupervised image hashing effective hashing for large scale image databases is a popular research area attracting much attention in computer vision and visual information retrieval several recent methods attempt to learn either graph embedding or semantic coding for fast and accurate applications in this paper a novel unsupervised framework termed evolutionary compact embedding ece is introduced to automatically learn the task specific binary hash codes it can be regarded as an optimization algorithm that combines the genetic programming gp and a boosting trick in our architecture each bit of ece is iteratively computed using a weak binary classification function which is generated through gp evolving by jointly minimizing its empirical risk with the adaboost strategy on a training set we address this as greedy optimization by embedding high dimensional data points into a similarity preserved hamming space with a low dimension we systematically evaluate ece on two data sets sift 1m and gist 1m showing the effectiveness and the accuracy of our method for a large scale similarity search

hybrid uncertainty propagation in structural acoustic systems based on the polynomial chaos expansion and dimension wise analysis abstract hybrid uncertainties are ubiquitous in the structural acoustic analysis and greatly affect the behaviors of the coupled system however requirements of small uncertainties and rewriting simulation codes for the numerical analysis are always necessary for current methods in this context a non intrusive dual layer analysis procedure i e a hybrid method of the polynomial chaos expansion and dimension wise analysis pce dw is proposed in this paper the pce procedure based on the sparse grid collocation strategy is utilized to handle the random uncertainty while the dw procedure is employed to evaluate the interval bounds of statistical characteristics of the system response the pce dw also applies to the structural acoustic analysis with only random or interval parameters the investigation of two structural acoustic systems with hybrid uncertainties finally demonstrates the accuracy efficiency and capability of the proposed method

hetero manifold regularisation for cross modal hashing recently cross modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi modal data to address both challenges in this paper we propose a novel method termed hetero manifold regularisation hmr to supervise the learning of hash functions for efficient cross modal search a hetero manifold integrates multiple sub manifolds defined by homogeneous data with the help of cross modal supervision information taking advantages of the hetero manifold the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero manifold furthermore a novel cumulative distance inequality defined on the hetero manifold is introduced to avoid the computational difficulty induced by the discreteness of hash codes by using the inequality cross modal hashing is transformed into a problem of hetero manifold regularised support vector learning therefore the performance of cross modal search can be significantly improved by seamlessly combining the integrated information of the hetero manifold and the strong generalisation of the support vector machine comprehensive experiments show that the proposed hmr achieve advantageous results over the state of the art methods in several challenging cross modal tasks

a remark on the period of the periodic solution in the lotka volterra system abstract an important consideration in the nonlinear predator prey problem of lotka volterra type is the determination of the period this paper gives a general expression for the period in terms of the given parameters in the lotka volterra system we also discuss the qualitative behavior of the period related to the energy level of the lotka volterra system

modeling the shape of the scene a holistic representation of the spatial envelope in this paper we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions the procedure is based on a very low dimensional representation of the scene that we term the spatial envelope we propose a set of perceptual dimensions naturalness openness roughness expansion ruggedness that represent the dominant spatial structure of a scene then we show that these dimensions may be reliably estimated using spectral and coarsely localized information the model generates a multidimensional space in which scenes sharing membership in semantic categories e g streets highways coasts are projected closed together the performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category

supervised matrix factorization hashing for cross modal retrieval the target of cross modal hashing is to embed heterogeneous multimedia data into a common low dimensional hamming space which plays a pivotal part in multimedia retrieval due to the emergence of big multimodal data recently matrix factorization has achieved great success in cross modal hashing however how to effectively use label information and local geometric structure is still a challenging problem for these approaches to address this issue we propose a cross modal hashing method based on collective matrix factorization which considers both the label consistency across different modalities and the local geometric consistency in each modality these two elements are formulated as a graph laplacian term in the objective function leading to a substantial improvement on the discriminative power of latent semantic features obtained by collective matrix factorization moreover the proposed method learns unified hash codes for different modalities of an instance to facilitate cross modal search and the objective function is solved using an iterative strategy the experimental results on two benchmark data sets show the effectiveness of the proposed method and its superiority over state of the art cross modal hashing methods

the transformation method for the simulation and analysis of systems with uncertain parameters in this paper the transformation method is introduced as a powerful approach for both the simulation and the analysis of systems with uncertain model parameters based on the concept of cuts the method represents a special implementation of fuzzy arithmetic that avoids the well known effect of overestimation which usually arises when fuzzy arithmetic is reduced to interval computation systems with uncertain model parameters can thus be simulated without any artificial widening of the simulation results as a by product of the implementation scheme the transformation method also provides a measure of influence to quantitatively analyze the uncertain system with respect to the effect of each uncertain model parameter on the overall uncertainty of the model output by this a special kind of sensitivity analysis can be defined on the basis of fuzzy arithmetic finally to show the efficiency of the transformation method the method is applied to the simulation and analysis of a model for the friction interface between the sliding surfaces of a bolted joint connection

nonstationary response of a nonlinear economic cycle model under random disturbance abstract stochastic mechanics is widely applied in the field of economics and econometrics one important application is to study economic fluctuations in economic cycle theory however nonstationary response of nonlinear economic cycles with random disturbance is less investigated this paper is devoted to studying the nonstationary response of a nonlinear economic cycle model first a nonlinear economic cycle model is developed according to the goodwin model and the puu nonlinear economic cycle model gaussian white noise is regarded as a spontaneous function in the developed nonlinear economic cycle model then a path integration method is adopted based on gauss legendre scheme and short time gaussian approximation for obtaining the nonstationary probability density function pdf of the nonlinear economic cycle model the obtained results are verified by the simulation result finally the evolution of the nonstationary pdf is studied in detail in the numerical analysis the effects of system parameters are compared and discussed the results show that the addition of the quadratic term of income velocity leads to the nonzero means of the pdfs of income and income velocity due to the presence of the quadratic and cubic terms these pdfs have non gaussian distributions

hybrid uncertain static analysis with random and interval fields abstract uncertain static analysis of an engineering structure with diverse type of non deterministic system parameter is investigated in this study unlike the traditional hybrid uncertain static analysis involving random and interval variables the concept of random and interval fields has been implemented to model the spatially dependent uncertainties associated with the system inputs a novel computational approach namely the extended unified interval stochastic sampling x uiss method is proposed to calculate the statistical characteristics i e mean and standard deviation of the extreme bounds i e lower and upper bounds of the concerned responses e g displacement and stress of engineering structure involving hybrid spatially dependent uncertainties subsequently by utilizing either parametric or nonparametric statistical analysis the probability density functions pdfs as well as the cumulative distribution functions cdfs of the extreme bounds of the concerned structural responses can be effectively established consequently the upper and lower bounds of either the concerned responses of the engineering structure at any particular percentile of probability or the structural reliability against any specified capacities can be effectively secured the applicability and effectiveness of the proposed computational analysis framework are illustrated through the numerical investigations on various examples

orbits in a stochastic goodwin lotka volterra model abstract this paper examines the cycling behavior of a deterministic and a stochastic version of the economic interpretation of the lotka volterra model the goodwin model we provide a characterization of orbits in the deterministic highly non linear model we then study a stochastic version with brownian noise introduced via a heterogeneous productivity factor existence conditions for a solution to the system are provided we prove that the system produces cycles around a unique equilibrium point in finite time for general volatility levels using stochastic lyapunov techniques for recurrent domains numerical insights are provided

non gaussian ornstein uhlenbeck based models and some of their uses in financial economics non gaussian processes of ornstein uhlenbeck ou type offer the possibility of capturing important distributional deviations from gaussianity and for flexible modelling of dependence structures this paper develops this potential drawing on and extending powerful results from probability theory for applications in statistical analysis their power is illustrated by a sustained application of ou processes within the context of finance and econometrics we construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive ou processes and we study these models in relation to financial data and theory

ornstein uhlenbeck processes for geophysical data analysis abstract in this work a three parameter stochastic process termed the gamma ornstein uhlenbeck process has been implemented to analyze geophysical data such non gaussian ornstein uhlenbeck processes offer the possibility of capturing important distributional deviations from gaussianity and make the model flexible of dependence structures it is shown that the gamma ornstein uhlenbeck process is a possible candidate for earthquake data modeling rigorous regression analysis is provided and based on that the first passage times are computed for different sets of data it is shown that this model may be used to estimate parameters related to some major events namely major earthquakes

interval fields to represent uncertainty on the output side of a static fe analysis abstract intervals have been used extensively for the representation of parametric uncertainty in finite element models when the available information is insufficient to build representative probabilistic models although the complexity of the interval concept is rather limited the numerical problem arising at the core of the interval finite element method was proven to be non trivial many research activities have focussed on this problem over the last decade still one of the principal shortcomings of the current interval finite element procedures is that they are intrinsically not capable of representing dependencies among uncertain input and or output quantities this poses important limitations on the applicability of the interval concept in non deterministic finite element analysis these limits arise in the model definition phase as well as in the post processing phase in order to cope with this problem this paper introduces the concept of interval fields the focus in this work is on the static analysis of mechanical structures with interval uncertainties in the context of the finite element method however the presented methodology is generic and can be readily extended to any kind of finite element problem including interval uncertainties in the first part of the paper the theoretical background of the concept is presented and it is shown how explicit and implicit interval fields can be used to represent dependent uncertain parameters in the model definition phase and in the post processing phase furthermore the applicability and accuracy of both types of interval fields are studied by means of a numerical validation example

evolution of predator prey systems described by a lotka volterra equation under random environment abstract in this paper we consider the evolution of a system composed of two predator prey deterministic systems described by lotka volterra equations in random environment it is proved that under the influence of telegraph noise all positive trajectories of such a system always go out from any compact set of int r 2 with probability one if two rest points of the two systems do not coincide in case where they have the rest point in common the trajectory either leaves from any compact set of int r 2 or converges to the rest point the escape of the trajectories from any compact set means that the system is neither permanent nor dissipative

reidentification by relative distance comparison matching people across nonoverlapping camera views at different locations and different times known as person reidentification is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time person reidentification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle lighting background clutter and occlusion to address these challenges most previous approaches aim to model and extract distinctive and reliable visual features however seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions from a distance is still an open and unsolved problem for person reidentification in this paper we formulate person reidentification as a relative distance comparison rdc learning problem in order to learn the optimal similarity measure between a pair of person images this approach avoids treating all features indiscriminately and does not assume the existence of some universally distinctive and reliable features to that end a novel relative distance comparison model is introduced the model is formulated to maximize the likelihood of a pair of true matches having a relatively smaller distance than that of a wrong match pair in a soft discriminant manner moreover in order to maintain the tractability of the model in large scale learning we further develop an ensemble rdc model extensive experiments on three publicly available benchmarking datasets are carried out to demonstrate the clear superiority of the proposed rdc models over related popular person reidentification techniques the results also show that the new rdc models are more robust against visual appearance changes and less susceptible to model overfitting compared to other related existing models

domain class consistency based transfer learning for image classification across domains distribution mismatch between the modeling data and the query data is a known domain adaptation issue in machine learning to this end in this paper we propose a l2 1 norm based discriminative robust kernel transfer learning dktl method for high level recognition tasks the key idea is to realize robust domain transfer by simultaneously integrating domain class consistency dcc metric based discriminative subspace learning kernel learning in reproduced kernel hilbert space and representation learning between source and target domain the dcc metric includes two properties domain consistency used to measure the between domain distribution discrepancy and class consistency used to measure the within domain class separability the essential objective of the proposed transfer learning method is to maximize the dcc metric which is equivalently to minimize the domain class inconsistency dcic such that domain distribution mismatch and class inseparability are well formulated and unified simultaneously the merits of the proposed method include 1 the robust sparse coding selects a few valuable source data with noises outliers removed during knowledge transfer and 2 the proposed dcc metric can pursue more discriminative subspaces of different domains as a result the maximum class separability is also well guaranteed extensive experiments on a number of visual datasets demonstrate the superiority of the proposed method over other state of the art domain adaptation and transfer learning methods

an interdependency index for the outputs of uncertain systems the study of mechanical systems with uncertain parameters is gaining increasing interest in the field of system analysis to provide an expedient model for the prediction of the system behavior making use of the transformation method the uncertain parameters of the system are modeled by fuzzy numbers in contrast to random numbers used in stochastic approaches as a result of this analysis a quantification of the overall uncertainty of the system outputs including a worst case scenario is obtained the inputs of the resulting fuzzy valued model are a priori uncorrelated but after the uncertainties are propagated through the model interdependency or interaction between the outputs may arise if such interdependency is neglected a misinterpretation of the results may occur for example in the case of applying uncertainty analysis in the early design phase of a product to determine the relevant design parameter space the interdependency between the design variables may reduce significantly the available part of the design space this paper proposes a measure of interdependency between the uncertain system outputs the interdependency index can be derived by a postprocessing of the data gained by the analysis with the transformation method such information can be obtained by a negligible amount of extra computation time

learning a multiple kernel similarity metric for kinship verification abstract kinship verification kv has recently caught much attention in the computer vision community due to its potential applications ranging from missing children search to social media analysis most of the related work focuses either on developing hand crafted feature representations to describe the faces or on learning the mahalanobis distance metric to measure the similarity between facial images instead in this paper we propose a novel multiple kernel similarity metric mksm in which different from the mahalanobis metric the similarity computation is essentially based on an implicit nonlinear feature transformation the overall mksm is a weighted combination of basic similarities and therefore possesses the capacity for feature fusion the basic similarities are derived from base kernels and local features and the weights are obtained by solving a constrained linear programming lp problem that originates from a large margin lm criterion particularly the lm criterion not only guarantees the generalization on unseen samples when the training set is small but also leads to sparsity in the weight vector which in turn boosts the efficiency at the prediction stage extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed method

integration of stochastic models by minimizing α divergence when there are a number of stochastic models in the form of probability distributions one needs to integrate them mixtures of distributions are frequently used but exponential mixtures also provide a good means of integration this letter proposes a one parameter family of integration called integration which includes all of these well known integrations these are generalizations of various averages of numbers such as arithmetic geometric and harmonic averages there are psychophysical experiments that suggest that integrations are used in the brain the divergence between two distributions is defined which is a natural generalization of kullback leibler divergence and hellinger distance and it is proved that integration is optimal in the sense of minimizing divergence the theory is applied to generalize the mixture of experts and the product of experts to the mixture of experts the predictive distribution is also stated in the bayesian framework