the daily computed weighted averaging basic reproduction number r0 k ωn for mers cov in south korea abstract in this paper we propose the daily computed weighted averaging basic reproduction number r 0 k n for middle east respiratory syndrome coronavirus mers cov outbreak in south korea may to july 2015 we use an sir model with piecewise constant parameters contact rate and removed rate we use the explicit euler s method for the solution of the sir model and a nonlinear least square fitting procedure for finding the best parameters in r 0 k n the parameters n k and w denote days from a reference date the number of days in averaging and a weighting factor respectively we perform a series of numerical experiments and compare the results with the real world data in particular using the predicted reproduction number based on the previous two consecutive reproduction numbers we can predict the future behavior of the reproduction number

bayesian dose response analysis for epidemiological studies with complex uncertainty in dose estimation most conventional risk analysis methods rely on a single best estimate of exposure per person which does not allow for adjustment for exposure related uncertainty here we propose a bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose our bayesian risk analysis method utilizes multiple realizations of sets vectors of doses generated by a two dimensional monte carlo simulation method that properly separates shared and unshared errors in dose estimation the exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in kazakhstan we assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods when the estimated doses contain relatively small amounts of uncertainty the bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression based methods of dose response analysis however when large and complex mixtures of shared and unshared uncertainties are present the bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression based risk analysis methods and better coverage that is a markedly increased capability to include the true risk coefficient within the 95 credible interval of the bayesian based risk estimate an evaluation of the dose response using our method is presented for an epidemiological study of thyroid disease following radiation exposure

revisiting node based sir models in complex networks with degree correlations abstract in this paper we consider two growing networks which will lead to the degree degree correlations between two nearest neighbors in the network when the network grows to some certain size we introduce an sir like disease such as pandemic influenza h1n1 09 to the population due to its rapid spread the population size changes slowly and thus the disease spreads on correlated networks with approximately fixed size to predict the disease evolution on correlated networks we first review two node based sir models incorporating degree correlations and an edge based sir model without considering degree correlation and then compare the predictions of these models with stochastic sir simulations respectively we find that the edge based model even without considering degree correlations agrees much better than the node based models incorporating degree correlations with stochastic sir simulations in many respects moreover simulation results show that for networks with positive correlation the edge based model provides a better upper bound of the cumulative incidence than the node based sir models whereas for networks with negative correlation it provides a lower bound of the cumulative incidence

global threshold dynamics of a stochastic differential equation sis model abstract in this paper we further investigate the global dynamics of a stochastic differential equation sis susceptible infected susceptible epidemic model recently proposed in gray et al 2011 8 we present a stochastic threshold theorem in term of a stochastic basic reproduction number r 0 s the disease dies out with probability one if r 0 s 1 and the disease is recurrent if r 0 s 1 we prove the existence and global asymptotic stability of a unique invariant density for the fokker planck equation associated with the sde sis model when r 0 s 1 in term of the profile of the invariant density we define a persistence basic reproduction number r 0 p and give a persistence threshold theorem the disease dies out with large probability if r 0 p 1 while persists with large probability if r 0 p 1 comparing the stochastic disease prevalence with the deterministic disease prevalence we discover that the stochastic prevalence is bigger than the deterministic prevalence if the deterministic basic reproduction number r 0 d 2 this shows that noise may increase severity of disease finally we study the asymptotic dynamics of the stochastic sis model as the noise vanishes and establish a sharp connection with the threshold dynamics of the deterministic sis model in term of a limit stochastic threshold theorem

on the potential of measurement error to induce differential bias on odds ratio estimates an example from radon epidemiology it is well established that odds ratios estimated by logistic regression are subject to bias if exposure is measured with error the dependence of this bias on exposure parameter values particularly for multiplicative measurement error and its implications in epidemiology are not however as fully acknowledged we have been motivated by a german west case control study on lung cancer and residential radon where restriction to a subgroup exhibiting larger mean and variance of exposure than the entire group has shown higher odds ratio estimates as compared to the full analysis by means of correction formulae and simulations we show that bias from additive classical type error depends on the exposure variance not on the exposure mean and that bias from multiplicative classical type error depends on the geometric standard deviation in other words on the coefficient of variation of exposure but not on the geometric mean of exposure bias from additive or multiplicative berkson type error is independent of exposure distribution parameters this indicates that there is a potential of differential bias between groups where these parameters vary such groups are commonly compared in epidemiology for example when the results of subgroup analyses are contrasted or meta analyses are performed for the german west radon study we show that the difference of measurement error bias between the subgroup and the entire group exhibits the same direction but not the same dimension as the observed results regarding meta analysis of five european radon studies we find that a study such as this german study will necessarily result in smaller odds ratio estimates than other studies due to the smaller exposure variance and coefficient of variation of exposure therefore disregard of measurement error can not only lead to biased estimates but also to inconsistent results and wrongly concluded effect differences between groups

epidemic spreading in correlated complex networks we study a dynamical model of epidemic spreading on complex networks in which there are explicit correlations among the node s connectivities for the case of markovian complex networks showing only correlations between pairs of nodes we find an epidemic threshold inversely proportional to the largest eigenvalue of the connectivity matrix that gives the average number of links which from a node with connectivity k go to nodes with connectivity k ensuremath numerical simulations on a correlated growing network model provide support for our conclusions

extinction and persistence of a stochastic nonlinear sis epidemic model with jumps abstract in this paper brownian motion and l e vy jumps are introduced to a sis type epidemic model with nonlinear incidence rate the dynamical behavior of the considered model is investigated in order to reveal the extinction and permanence of the disease two threshold values r 0 r 0 are showed we find that if r 0 1 the disease may die out and when r 0 1 the disease may be persistent finally the numerical simulations are presented to illustrate our mathematical results

two group sir epidemic model with stochastic perturbation a stochastic two group sir model is presented in this paper the existence and uniqueness of its nonnegative solution is obtained and the solution belongs to a positively invariant set furthermore the globally asymptotical stability of the disease free equilibrium is deduced by the stochastic lyapunov functional method if r 0 1 which means the disease will die out while if r 0 1 we show that the solution is fluctuating around a point which is the endemic equilibrium of the deterministic model in time average in addition the intensity of the fluctuation is proportional to the intensity of the white noise when the white noise is small we consider the disease will prevail at last we illustrate the dynamic behavior of the model and their approximations via a range of numerical experiments

periodic solution for a stochastic nonautonomous sir epidemic model with logistic growth abstract in this paper we analyze the dynamics of a stochastic nonautonomous sir epidemic model in which population growth is subject to logistic growth in absence of disease for the periodic system we present sufficient conditions for persistence of the epidemic and in the case of persistence by constructing some suitable lyapunov functions we show that there is at least one nontrivial positive periodic solution one of the most important findings is that random perturbations may be beneficial to formate the periodic solution to the stochastic nonautonomous sir epidemic model

kernelized locality sensitive hashing fast retrieval methods are critical for many large scale and data driven vision applications recent work has explored ways to embed high dimensional features or complex distance functions into a low dimensional hamming space where items can be efficiently searched however existing methods do not apply for high dimensional kernelized data when the underlying feature embedding for the kernel is unknown we show how to generalize locality sensitive hashing to accommodate arbitrary kernel functions making it possible to preserve the algorithm s sublinear time similarity search guarantees for a wide class of useful similarity functions since a number of successful image based kernels have unknown or incomputable embeddings this is especially valuable for image retrieval tasks we validate our technique on several data sets and show that it enables accurate and fast performance for several vision problems including example based object classification local feature matching and content based retrieval

stationary distribution and threshold dynamics of a stochastic sirs model with a general incidence abstract in this paper we consider a stochastic epidemic model with relapse cure and a non linear incidence rate function firstly we show the existence and uniqueness of a global positive solution then in terms of a stochastic threshold r s 0 we prove the extinction of the disease when r s 0 1 we also establish the persistence in mean of the epidemic in the case of r s 0 1 furthermore we prove the existence of a stationary distribution and its asymptotic stability finally we present numerical simulations for the stochastic model to support the theoretical results

stability of a stochastic sir system abstract we propose a stochastic sir model with or without distributed time delay and we study the stability of disease free equilibrium the numerical simulation of the stochastic sir model shows that the introduction of noise modifies the threshold of system for an epidemic to occur and the threshold stochastic value is found

sir model with general distribution function in the infectious period abstract the susceptible infected removed or sir model as it was formulated by kermack and mckendrick is the key model for epidemic dynamics most applications of such a basic scheme use a constant rate for the removal term however that assumption corresponds to the rather unrealistic exponential distribution of infectious times on the other hand recent approaches like numerical simulations frequently assume a fixed and uniform duration for the infectious state which is unrealistic too the extreme assumptions in those different schemes are a hurdle that can frustrate any intention of drawing comparison between results from them in the present contribution we study the delay equations for the sir model comparing the solutions for many typical cases with the simulation counterpart and with the standard sir model using delay equations where each infected individual is removed at a specific time after being infected the dynamics for the infected and susceptible agree almost exactly with the numerical implementation even in the general case of distributed infective periods the agreement is excellent

dynamics of a stochastic siqr epidemic model with standard incidence abstract in this paper a stochastic siqr epidemic model with quarantine and standard incidence is investigated we first consider the existence and uniqueness of the global positive solution to the stochastic system further we obtain conclusions that the large noise can make the disease die out exponentially and the stationary distribution will exist under certain conditions

stationary distribution and extinction of a stochastic seir epidemic model with standard incidence abstract in this paper we consider a stochastic seir epidemic model with standard incidence by constructing suitable stochastic lyapunov functions we establish sufficient conditions for the existence of ergodic stationary distribution to the model our result does not depend on the positive endemic equilibrium e of the deterministic system which improves the previous result to a greater extent moreover we also establish sufficient conditions for extinction of the disease

measurement error in the explanatory variable of a binary regression regression calibration and integrated conditional likelihood in studies of residential radon and lung cancer in epidemiology one approach to investigating the dependence of disease risk on an explanatory variable in the presence of several confounding variables is by fitting a binary regression using a conditional likelihood thus eliminating the nuisance parameters when the explanatory variable is measured with error the estimated regression coefficient is biased usually towards zero motivated by the need to correct for this bias in analyses that combine data from a number of case control studies of lung cancer risk associated with exposure to residential radon two approaches are investigated both employ the conditional distribution of the true explanatory variable given the measured one the method of regression calibration uses the expected value of the true given measured variable as the covariate the second approach integrates the conditional likelihood numerically by sampling from the distribution of the true given measured explanatory variable the two approaches give very similar point estimates and confidence intervals not only for the motivating example but also for an artificial data set with known properties these results and some further simulations that demonstrate correct coverage for the confidence intervals suggest that for studies of residential radon and lung cancer the regression calibration approach will perform very well so that nothing more sophisticated is needed to correct for measurement error

a stochastic threshold for an epidemic model with beddington deangelis incidence delayed loss of immunity and levy noise perturbation abstract in this paper we study a stochastic epidemic model with delayed loss of immunity and levy noise perturbation we establish the uniqueness of the global positive solution we also investigate the extinction and the persistence in mean of the disease

hashing with angular reconstructive embeddings large scale search methods are increasingly critical for many content based visual analysis applications among which hashing based approximate nearest neighbor search techniques have attracted broad interests due to their high efficiency in storage and retrieval however existing hashing works are commonly designed for measuring data similarity by the euclidean distances in this paper we focus on the problem of learning compact binary codes using the cosine similarity specifically we proposed novel angular reconstructive embeddings are method which aims at learning binary codes by minimizing the reconstruction error between the cosine similarities computed by original features and the resulting binary embeddings furthermore we devise two efficient algorithms for optimizing our are in continuous and discrete manners respectively we extensively evaluate the proposed are on several large scale image benchmarks the results demonstrate that are outperforms several state of the art methods

epidemic incidence in correlated complex networks a few years ago watts and strogatz 1 introduced a model able to produce networks with properties of both regu lar lattices and random graphs with small diameter their model soon led to a burst of activity in the field 2 3 further spurred by barabasi and co workers who found that many seemingly diverse systems share several topological proper ties such as a power law behavior in their connectivity dis tributions when represented as networks 4 these complex networks are formed by a set of many elements or nodes that are linked together through edges or links if they in teract directly empirical evidence supports that in notable networks such as metabolic or communication webs the probability p k that any node has k links to other nodes is distributed accordingly to a power law p k k 2g 5 7

distributed adaptive binary quantization for fast nearest neighbor search hashing has been proved an attractive technique for fast nearest neighbor search over big data compared with the projection based hashing methods prototype based ones own stronger power to generate discriminative binary codes for the data with complex intrinsic structure however existing prototype based methods such as spherical hashing and k means hashing still suffer from the ineffective coding that utilizes the complete binary codes in a hypercube to address this problem we propose an adaptive binary quantization abq method that learns a discriminative hash function with prototypes associated with small unique binary codes our alternating optimization adaptively discovers the prototype set and the code set of a varying size in an efficient way which together robustly approximate the data relations our method can be naturally generalized to the product space for long hash codes and enjoys the fast training linear to the number of the training data we further devise a distributed framework for the large scale learning which can significantly speed up the training of abq in the distributed environment that has been widely deployed in many areas nowadays the extensive experiments on four large scale up to 80 million data sets demonstrate that our method significantly outperforms state of the art hashing methods with up to 58 84 performance gains relatively

the mathematics of infectious diseases many models for the spread of infectious diseases in populations have been analyzed mathematically and applied to specific diseases threshold theorems involving the basic reproduction number r 0 the contact number sigma and the replacement number r are reviewed for the classic sir epidemic and endemic models similar results with new expressions for r 0 are obtained for mseir and seir endemic models with either continuous age or age groups values of r 0 and sigma are estimated for various diseases including measles in niger and pertussis in the united states previous models with age structure heterogeneity and spatial structure are surveyed

evolutionary compact embedding for large scale image classification effective dimensionality reduction is a classical research area for many large scale analysis tasks in computer vision several recent methods attempt to learn either graph embedding or binary hashing for fast and accurate applications in this paper we propose a novel framework to automatically learn the task specific compact coding called evolutionary compact embedding ece which can be regarded as an optimization algorithm combining genetic programming gp and a boosting trick as an evolutionary computation methodology gp can solve problems inspired by natural evolution without any prior knowledge of the solutions in our evolutionary architecture each bit of ece is iteratively computed using a 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 leading to small hamming distances for similar samples and large distances for dissimilar samples we then evaluate ece on four image datasets usps digital hand writing cmu pie face cifar 10 tiny image and sun397 scene showing the accurate and robust performance of our method for large scale image classification

the performance of functional methods for correcting non gaussian measurement error within poisson regression corrected excess risk of lung cancer mortality in relation to radon exposure among french uranium miners a broad variety of methods for measurement error me correction have been developed but these methods have rarely been applied possibly because their ability to correct me is poorly understood we carried out a simulation study to assess the performance of three error correction methods two variants of regression calibration the substitution method and the estimation calibration method and the simulation extrapolation simex method features of the simulated cohorts were borrowed from the french uranium miners cohort in which exposure to radon had been documented from 1946 to 1999 in the absence of me correction we observed a severe attenuation of the true effect of radon exposure with a negative relative bias of the order of 60 on the excess relative risk of lung cancer death in the main scenario considered that is when me characteristics previously determined as most plausible from the french uranium miners cohort were used both to generate exposure data and to correct for me at the analysis stage all three error correction methods showed a noticeable but partial reduction of the attenuation bias with a slight advantage for the simex method however the performance of the three correction methods highly depended on the accurate determination of the characteristics of me in particular we encountered severe overestimation in some scenarios with the simex method and we observed lack of correction with the three methods in some other scenarios for illustration we also applied and compared the proposed methods on the real data set from the french uranium miners cohort study copyright 2012 john wiley sons ltd

correction of logistic regression relative risk estimates and confidence intervals for systematic within person measurement error errors in the measurement of exposure that are independent of disease status tend to bias relative risk estimates and other measures of effect in epidemiologic studies toward the null value two methods are provided to correct relative risk estimates obtained from logistic regression models for measurement errors in continuous exposures within cohort studies that may be due to either random unbiased within person variation or to systematic errors for individual subjects these methods require a separate validation study to estimate the regression coefficient lambda relating the surrogate measure to true exposure in the linear approximation method the true logistic regression coefficient beta is estimated by beta lambda where beta is the observed logistic regression coefficient based on the surrogate measure in the likelihood approximation method a second order taylor series expansion is used to approximate the logistic function enabling closed form likelihood estimation of beta confidence intervals for the corrected relative risks are provided that include a component representing error in the estimation of lambda based on simulation studies both methods perform well for true odds ratios up to 3 0 for higher odds ratios the likelihood approximation method was superior with respect to both bias and coverage probability an example is provided based on data from a prospective study of dietary fat intake and risk of breast cancer and a validation study of the questionnaire used to assess dietary fat intake

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

some aspects of measurement error in explanatory variables for continuous and binary regression models a simple form of measurement error model for explanatory variables is studied incorporating classical and berkson cases as particular forms and allowing for either additive or multiplicative errors the work is motivated by epidemiological problems and therefore consideration is given not only to continuous response variables but also to logistic regression models the possibility that different individuals in a study have errors of different types is also considered the relatively simple estimation procedures proposed for use with cohort data and case control data are checked by simulation under the assumption of various error structures the results show that even in situations where conventional analysis yields slope estimates that are on average attenuated by a factor of approximately 50 per cent estimates obtained using the proposed amended likelihood functions are within 5 per cent of their true values the work was carried out to provide a method for the analysis of lung cancer risk following residential radon exposure but it should be applicable to a wide variety of situations

additive logistic regression a statistical view of boosting boosting is one of the most important recent developments in classification methodology boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced for many classification algorithms this simple strategy results in dramatic improvements in performance we show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles namely additive modeling and maximum likelihood for the two class problem boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum bernoulli likelihood as a criterion we develop more direct approximations and show that they exhibit nearly identical results to boosting direct multiclass generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multiclass generalizations of boosting in most situations and far superior in some we suggest a minor modification to boosting that can reduce computation often by factors of 10 to 50 finally we apply these insights to produce an alternative formulation of boosting decision trees this approach based on best first truncated tree induction often leads to better performance and can provide interpretable descriptions of the aggregate decision rule it is also much faster computationally making it more suitable to large scale data mining applications

query adaptive hash code ranking for large scale multi view visual search hash based nearest neighbor search has become attractive in many applications however the quantization in hashing usually degenerates the discriminative power when using hamming distance ranking besides for large scale visual search existing hashing methods cannot directly support the efficient search over the data with multiple sources and while the literature has shown that adaptively incorporating complementary information from diverse sources or views can significantly boost the search performance to address the problems this paper proposes a novel and generic approach to building multiple hash tables with multiple views and generating fine grained ranking results at bitwise and tablewise levels for each hash table a query adaptive bitwise weighting is introduced to alleviate the quantization loss by simultaneously exploiting the quality of hash functions and their complement for nearest neighbor search from the tablewise aspect multiple hash tables are built for different data views as a joint index over which a query specific rank fusion is proposed to rerank all results from the bitwise ranking by diffusing in a graph comprehensive experiments on image search over three well known benchmarks show that the proposed method achieves up to 17 11 and 20 28 performance gains on single and multiple table search over the state of the art methods

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

prospective analysis of logistic case control studies abstract in a classical case control study prentice and pyke proposed to ignore the study design and instead base estimation and inference on a random sampling i e prospective formulation we generalize this prospective formulation of case control studies to include multiplicative models stratification missing data measurement error robustness and other examples the resulting estimators which ignore the case control study aspect and instead are based on a random sampling formulation are typically consistent for nonintercept parameters and are asymptotically normally distributed we derive the resulting asymptotic covariance matrix of the parameter estimates the covariance matrix obtained by ignoring the case control sampling scheme and using prospective formulas instead is shown to be at worst asymptotically conservative and asymptotically correct in a variety of problems a simple sufficient condition guaranteeing the latter is obtained

the threshold of a stochastic sis epidemic model with vaccination this paper is concerned with the long time behavior of a stochastic sis epidemic model with vaccination sufficient conditions for extinction and persistence in mean are obtained we find a threshold of the stochastic model which determines the outcome of the disease in case the white noises are small at last some numerical simulations are carried out to support our results

likelihood calculations for matched case control studies and survival studies with tied death times summary a recursion is presented which permits rapid computation of a conditional likelihood function which arises in matched case control studies and survival studies with tied death times an example with seven parameters and ten large strata the largest of which contains 29 cases and 52 controls illustrates the feasibility of the conditional maximum likelihood solution these methods may be specialized to permit rapid calculation of the conditional maximum likelihood estimate of the common odds ratio for several 2 x 2 tables

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

the spillover effects between natural gas and crude oil markets the correlation network analysis based on multi scale approach abstract this paper proposes a new compound model to investigate the dynamic linkage mechanism between the natural gas and crude oil markets from the multi scale perspective in the proposed model two main steps are involved multi scale analysis and network research based on the bivariate empirical mode decomposition bemd and fine to coarse algorithm the multi scale analysis ensures the relationship between price fluctuations can be studied under three different time scales which corresponding to market disequilibrium significant events and long term trend by integrating the grey correlation degree and coarse gaining algorithm the network research reveals the dynamic spillover effects of the two markets at different time scales to capture the different linked characteristic in different periods the sample data of henry hub and wti spot prices from 1997 to 2017 is divided into three periods we transfer the daily correlations between the price fluctuations with specific time scale into the correlation patterns and establish the network based on their transmission relations the crucial correlation patterns and the significant transmission relations are revealed by some network indicators and the proposed approach in this paper can be applied to other field of research

a metapopulation model for the population dynamics of anopheles mosquito a reactiondiffusion model for the dynamics of anopheles mosquito in a network is designed a fixed point theorem is used to establish the existence uniqueness of a nontrivial equilibrium a threshold parameter is computed sensitivity analysis of parameters and variables are given the monotone operator theory is used to prove the gas of trivial and positive equilibriums assessment of mosquito dispersal patch heterogeneity on the dynamics of mosquito is given a more robust assessment of malaria control will come from a better understanding of the distribution and connectivity of breeding and blood feeding sites spatial heterogeneity of mosquito resources such as hosts and breeding sites affects mosquito dispersal behavior this paper analyzes and simulates the spreading of anopheles mosquito on a complex metapopulation that is networks of populations connected by migratory flows whose configurations are described in terms of connectivity distribution of nodes patches and the conditional probabilities of connections between nodes we examine the impacts of vector dispersal on the persistence and extinction of a mosquito population in both homogeneous and heterogeneous landscapes for uncorrelated networks in a homogeneous landscape we derive an explicit formula of the basic offspring number r0 m using the theory of monotone operators we obtain sufficient conditions for the global asymptotic stability of equilibria precisely the value 1 of the basic offspring number is a forward bifurcation for the dynamics of anopheles mosquito with the trivial mosquito free equilibrium point being globally asymptotically stable gas when r0 m 1 and one stable nontrivial mosquito persistent equilibrium point being born with well determined basins of attraction when r0 m 1 theoretical results are numerically supported and the impact of the migration of mosquitoes are discussed through global sensitivity analysis and numerical simulations

a method of simulated moments for estimation of discrete response models without numerical integration this paper proposes a simple modification of a conventional method of moments estimator for a discrete response model replacing response probabilities that require numerical integration with estimators obtained by monte carlo simulation this method of simulated moments msm does not require precise estimates of these probabilities for consistency and asymptotic normality relying instead on the law of large numbers operating across observations to control simulation error and hence can use simulations of practical size the method is useful for models such as high dimensional multinomial probit mnp where computation has restricted applications

latent structure preserving hashing aiming at efficient similarity search hash functions are designed to embed high dimensional feature descriptors to low dimensional binary codes such that similar descriptors will lead to binary codes with a short distance in the hamming space it is critical to effectively maintain the intrinsic structure and preserve the original information of data in a hashing algorithm in this paper we propose a novel hashing algorithm called latent structure preserving hashing lsph with the target of finding a well structured low dimensional data representation from the original high dimensional data through a novel objective function based on nonnegative matrix factorization nmf with their corresponding kullback leibler divergence of data distribution as the regularization term via exploiting the joint probabilistic distribution of data lsph can automatically learn the latent information and successfully preserve the structure of high dimensional data to further achieve robust performance with complex and nonlinear data in this paper we also contribute a more generalized multi layer lsph ml lsph framework in which hierarchical representations can be effectively learned by a multiplicative up propagation algorithm once obtaining the latent representations the hash functions can be easily acquired through multi variable logistic regression experimental results on three large scale retrieval datasets i e sift 1m gist 1m and 500 k tinyimage show that ml lsph can achieve better performance than the single layer lsph and both of them outperform existing hashing techniques on large scale data

stochastic regime switching sir model driven by levy noise abstract we propose a new stochastic regime switching sir model driven by levy noise a unique global positive solution is obtained under some appropriate conditions moreover we investigate the asymptotic behavior of the stochastic sir model with jumps under regime switching

dynamics for a class of stochastic sis epidemic models with nonlinear incidence and periodic coefficients abstract in this paper we investigate the dynamics of a class of periodic stochastic sis epidemic models with general nonlinear incidence f s i some sufficient conditions on the permanence in the mean and extinction of positive solutions with probability one are established by using the khasminskii s boundary periodic markov processes the existence of stochastic nontrivial periodic solution for the models is also obtained the numerical simulations are given to illustrate the main theoretical results and some interesting conjectures are presented

random graphs with arbitrary degree distributions and their applications recent work on the structure of social networks and the internet has focused attention on graphs with distributions of vertex degree that are significantly different from the poisson degree distributions that have been widely studied in the past in this paper we develop in detail the theory of random graphs with arbitrary degree distributions in addition to simple undirected unipartite graphs we examine the properties of directed and bipartite graphs among other results we derive exact expressions for the position of the phase transition at which a giant component first forms the mean component size the size of the giant component if there is one the mean number of vertices a certain distance away from a randomly chosen vertex and the average vertex vertex distance within a graph we apply our theory to some real world graphs including the worldwide web and collaboration graphs of scientists and fortune 1000 company directors we demonstrate that in some cases random graphs with appropriate distributions of vertex degree predict with surprising accuracy the behavior of the real world while in others there is a measurable discrepancy between theory and reality perhaps indicating the presence of additional social structure in the network that is not captured by the random graph

are randomly grown graphs really random we analyze a minimal model of a growing network at each time step a new vertex is added then with probability ensuremath delta two vertices are chosen uniformly at random and joined by an undirected edge this process is repeated for t time steps in the limit of large t the resulting graph displays surprisingly rich characteristics in particular a giant component emerges in an infinite order phase transition at ensuremath delta 1 8 at the transition the average component size jumps discontinuously but remains finite in contrast a static random graph with the same degree distribution exhibits a second order phase transition at ensuremath delta 1 4 and the average component size diverges there these dramatic differences between grown and static random graphs stem from a positive correlation between the degrees of connected vertices in the grown graph older vertices tend to have higher degree and to link with other high degree vertices merely by virtue of their age we conclude that grown graphs however randomly they are constructed are fundamentally different from their static random graph counterparts

threshold behavior in a stochastic sir epidemic model with logistic birth abstract in this paper we consider a stochastic sir epidemic model with logistic birth by using the stochastic lyapunov function method we show that the stochastic basic reproduction number r 0 s can be used to determine the threshold dynamics of the stochastic system if r 0 s 1 we establish sufficient conditions for the existence of a stationary distribution of the positive solutions to the model while if r 0 s 1 under some extra conditions we obtain sufficient conditions for extinction of the disease finally some examples and numerical simulations are provided to illustrate the theoretical results

epidemic spreading in a variety of scale free networks we have shown that the epidemic spreading in scale free networks is very sensitive to the statistics of degree distribution characterized by the index gamma the effective spreading rate lambda the social strategy used by individuals to choose a partner and the policy of administrating a cure to an infected node depending on the interplay of these four factors the stationary fractions of infected population f gamma as well as the epidemic threshold properties can be essentially different we have given an example of the evolutionary scale free network which is disposed to the spreading and the persistence of infections at any spreading rate lambda 0 for any gamma probably it is impossible to obtain a simple immunization program that can be simultaneously effective for all types of scale free networks we have also studied the dynamical solutions for the evolution equation governed by the epidemic spreading in scale free networks and found that for the case of vanishingly small cure rate delta 1 the initial configuration of infected nodes would feature the solution for very long times

dynamical behavior of a stochastic sei epidemic model with saturation incidence and logistic growth abstract in this paper we investigate the dynamical behavior of a stochastic sei epidemic model with saturation incidence and logistic growth firstly by constructing a suitable stochastic lyapunov function we establish sufficient conditions for the existence and uniqueness of an ergodic stationary distribution of the solutions to the model then we obtain sufficient conditions for extinction of the disease in two cases that is the first case is that the susceptible population survival and infected and exposed populations extinction the second case is that all the populations extinction

stability of sirs system with random perturbations epidemiological models with bilinear incidence rate si usually have an asymptotically stable trivial equilibrium corresponding to the disease free state or an asymptotically stable non trivial equilibrium i e interior equilibrium corresponding to the endemic state in this paper we consider an epidemiological model which is an sirs model with or without distributed time delay influenced by random perturbations we present the stability conditions of the disease free equilibrium of the associated stochastic sirs system

stationary distribution and extinction of a stochastic sirs epidemic model with standard incidence abstract in this paper we consider a stochastic sirs epidemic model with standard incidence by constructing suitable stochastic lyapunov function we establish sufficient conditions for the existence of ergodic stationary distribution of the model moreover we also establish sufficient conditions for extinction of the disease

threshold behavior of a stochastic sis model with levy jumps in this paper the dynamics of a stochastic sis model with levy jumps are investigated we first prove that this model has a unique global positive solution starting from the positive initial value then taking the accumulated jump size into account we find a threshold of the model denoted by r 0 which completely determines the extinction and prevalence of the disease if r 0 1 the disease dies out exponentially with probability one if r 0 1 the solution of the model tends to a point in time average which leads to the stochastical persistence of the disease from the view of epidemiology the existence of threshold is useful in determining treatment strategies and forecasting epidemic dynamics moreover we find that levy noise can suppress disease outbreak finally we introduce some numerical simulations to support the main results obtained