volume and surface integral equations for electromagnetic scattering by a dielectric body we derive and analyze two equivalent integral formulations for the time harmonic electromagnetic scattering by a dielectric object one is a volume integral equation vie with a strongly singular kernel and the other one is a coupled surface volume system of integral equations with weakly singular kernels the analysis of the coupled system is based on standard fredholm integral equations and it is used to derive properties of the volume integral equation

dcca cross correlation coefficient quantifying level of cross correlation abstract in this paper a new coefficient is proposed with the objective of quantifying the level of cross correlation between nonstationary time series this cross correlation coefficient is defined in terms of the dfa method and the dcca method the implementation of this cross correlation coefficient will be illustrated with selected time series

cross correlations between baltic dry index and crude oil prices this paper examines the cross correlation properties of baltic dry index bdi and crude oil prices using cross correlation statistics test and multifractal detrended cross correlation analysis mf dcca the empirical results show that the cross correlations between bdi and crude oil prices are significantly multifractal by introducing the concept of a crossover we find that the cross correlations are strongly persistent in the short term and weakly anti persistent in the long term moreover cross correlations of all kinds of fluctuations are persistent in the short time while cross correlations of small fluctuations are persistent and those of large fluctuations are anti persistent in the long term we have also verified that the multifractality of the cross correlations of bdi and crude oil prices is both attributable to the persistence of fluctuations of time series and fat tailed distributions

principal warps thin plate splines and the decomposition of deformations the decomposition of deformations by principal warps is demonstrated the method is extended to deal with curving edges between landmarks this formulation is related to other applications of splines current in computer vision how they might aid in the extraction of features for analysis comparison and diagnosis of biological and medical images in indicated

statistical analysis of digital images of periodic fibrous structures using generalized hurst exponent distributions abstract distinction of diverse two dimensional periodic structures can be based on a large number of methods and parameters while the quantitative description of differences between similar samples is usually difficult this article aims by the use of statistical random walk in a generalized q order dimensional space at introducing a methodology to qualify the networked structures on the basis of exemplary textile samples the presented results were obtained at 1 bit monochromatic maps obtained from optical microscopic pictures significant features of samples were represented by the obtained distributions of hurst exponents and shannon entropy calculations

cross correlations between brazilian biofuel and food market ethanol versus sugar abstract we investigate intrinsic correlations between brazilian energy ethanol and food market sugar by applying recently introduced method detrended partial cross correlation analysis dpcca on returns and volatility series of sugar ethanol and crude oil prices the results show that intrinsic cross correlations between ethanol and sugar are positive and increase with time scale for both returns and volatility indicating that the diversion of sugar cane for biofuel production due to the variations in ethanol prices affects more sugar prices than the price movements in external energy market ethanol oil volatility series and sugar oil return series do not show correlations for small scales while at larger scales intrinsic correlations become negative the intrinsic correlations in oil ethanol return series and oil sugar volatility series show similar behavior as corresponding ethanol sugar series indicating the existence of strong correlations between sugar and ethanol price variations

on the computation of power in volume integral equation formulations we present simple and stable formulas for computing power including absorbed radiated scattered and extinction power in current based volume integral equation formulations the proposed formulas are given in terms of vector matrix vector products of quantities found solely in the associated linear system in addition to their efficiency the derived expressions can guarantee the positivity of the computed power we also discuss the application of poynting s theorem for the case of sources immersed in dissipative materials the formulas are validated against results obtained both with analytical and numerical methods for scattering and radiation benchmark cases

current based volume integral equation formulation for bianisotropic materials the electric and magnetic current volume integral equation formulation is developed for the electromagnetic scattering analysis of arbitrarily shaped 3 d bianisotropic objects the eigenvalue spectrum of the operator is derived and analyzed in the case of bi isotropic media the effect of inhomogeneity and bianisotropy on the spectrum and convergence of iterative solutions is investigated numerically

detecting and quantifying cross correlations by analogous multifractal height cross correlation analysis abstract a new algorithm analogous multifractal height cross correlation analysis amf hxa is proposed in this paper our novel method takes into consideration of both the fluctuation information and the sign information in the corresponding cross covariance function numerical tests on artificially simulated series and real world series are performed to demonstrate that our method can accurately detect long range cross correlations for two simultaneously recorded series a new cross correlation coefficient is also defined to quantify the levels of cross correlation between two series

chaos based nonlinear analysis to study cardiovascular responses to changes in posture abstract cardiovascular responses to changes in posture have been the focus of numerous investigations in the past analysis of ecg and arterial blood pressure abp data for different postures obtained from physionet was done from a new perspective of analyzing nonlinear time series having multifractal features multifractal detrended fluctuation analysis mfdfa and also the cross correlation between ecg and bp with another technique multifractal detrended cross correlation analysis mfdxa reveals ecg and abp time series of different subjects to be multifractal in nature with different degree of complexity different degree of auto and cross correlation for both ecg and abp signals is also observed for all subjects further significant information obtained from the cross correlation parameters obtained from mfdxa analysis is very important and relevant particularly for understanding the dynamics of orthostatic stress

characterization of flow pattern transitions for horizontal liquid liquid pipe flows by using multi scale distribution entropy in coupled 3d phase space abstract horizontal oil water two phase flows often exist in many industrial processes uncovering the dynamic mechanism of the flow pattern transition is of great significance for modeling the flow parameters in this study we propose a method called multi scale distribution entropy msde in a coupled 3d phase space and use it to characterize the flow pattern transitions in horizontal oil water two phase flows firstly the proposed msde is validated with lorenz system and arfima processes interestingly it is found that the msde is dramatically associated with the cross correlations of the coupled time series then through conducting the experiment of horizontal oil water two phase flows the upstream and downstream flow information is collected using a conductance cross correlation velocity probe the coupled cross correlated signals are investigated using the msde method and the results indicate that the msde is an effective tool uncovering the complex dynamic behaviors of flow pattern transitions

quantifying the cross correlations between online searches and bitcoin market abstract in this paper we quantify the cross correlations between google trends and bitcoin market by employing the multifractal detrended cross correlation analysis mf dcca method we find that the change of google trends cgt and bitcoin market i e returns and changes of volume are overall significantly cross correlated based on the cross correlation test in particular the empirical results show that 1 there exist power law cross correlations between cgt and bitcoin returns as well as cgt and changes of volume 2 the cross correlations between cgt and returns have a higher degree of multifractal in the long term and weak multifractal in the short term while the cross correlations between cgt and change of volume show the opposite trend 3 with the rolling window analysis we further find that there is a decrease trend for the cross correlations between cgt and bitcoin returns over time and the cross correlations scaling exponents are less than 0 5 which indicate that they are both anti persistent cross correlated

autocorrelation and cross correlation in time series of homicide and attempted homicide abstract we propose in this paper to establish the relationship between homicides and attempted homicides by a non stationary time series analysis this analysis will be carried out by detrended fluctuation analysis dfa detrended cross correlation analysis dcca and dcca cross correlation coefficient d c c a n through this analysis we can identify a positive cross correlation between homicides and attempted homicides at the same time looked at from the point of view of autocorrelation dfa this analysis can be more informative depending on time scale for short scale days we cannot identify auto correlations on the scale of weeks dfa presents anti persistent behavior and for long time scales n 90 days dfa presents a persistent behavior finally the application of this new type of statistical analysis proved to be efficient and in this sense this paper can contribute to a more accurate descriptive statistics of crime

nonlinear stochastic exclusion financial dynamics modeling and time dependent intrinsic detrended cross correlation abstract in attempt to reproduce price dynamics of financial markets a stochastic agent based financial price model is proposed and investigated by stochastic exclusion process the exclusion process one of interacting particle systems is usually thought of as modeling particle motion with the conserved number of particles in a continuous time markov process in this work the process is utilized to imitate the trading interactions among the investing agents in order to explain some stylized facts found in financial time series dynamics to better understand the correlation behaviors of the proposed model a new time dependent intrinsic detrended cross correlation tdi dcc is introduced and performed also the autocorrelation analyses are applied in the empirical research furthermore to verify the rationality of the financial price model the actual return series are also considered to be comparatively studied with the simulation ones the comparison results of return behaviors reveal that this financial price dynamics model can reproduce some correlation features of actual stock markets

the application of the multifractal cross correlation analysis methods in radar target detection within sea clutter abstract many complex systems generate multifractal time series which are long range cross correlated this paper introduces three multifractal cross correlation analysis methods such as multifractal cross correlation analysis based on the partition function approach mfxpf multifractal detrended cross correlation analysis mfdcca methods based on detrended fluctuation analysis mfxdfa and detrended moving average analysis mfxdma which only consider one moment order we do comparative analysis of the artificial time series binomial multiplicative cascades and cantor sets with different probabilities by these methods then we do a feasibility test of the fixed threshold target detection within sea clutter by applying the multifractal cross correlation analysis methods to the ipix radar sea clutter data the results show that it is feasible to use the method of the fixed threshold based on the multifractal feature parameter f by the mfxpf and mfxdfa 1 methods at last we give the main conclusions and provide a valuable reference on how to choose the multifractal algorithms the detection parameters and the target detection methods within sea clutter in practice

multiscale multifractal detrended fluctuation analysis of multivariate time series abstract this work extends the multivariate multifractal detrended fluctuation analysis mv mfdfa method to multiscale case named multiscale multivariate multifractal detrended fluctuation analysis mmv mfdfa the benefits of the proposed approach are illustrated by numerical simulations on synthetic multivariate processes furthermore the proposed mmv mfdfa method is applied to the fractal auto correlation analysis of six pollutants pm2 5 pm10 so2 no2 co and o3 hourly data in different seasons the results show that the seasonal periodicity has robust impact on the auto correlation of pollutants in spring and summer besides we also find that the pollutants in the four seasons possess strong multifractal auto correlation nature even after the removal of the seasonal pattern finally the source of multifractality among more than two series is also discussed and some interesting results are obtained pm2 5 not only dominates the underlying evolution process in fall and winter but also is more correlated to the other pollutants than the other ones to each other except in spring the proposed mmv mfdfa methodology can provide reliable ways of measuring the fractal auto correlation properties of multivariate series and it can be applied to any system with multiple data channels

the inefficiency of cryptocurrency and its cross correlation with dow jones industrial average abstract as a form of cryptocurrency the issue of informational efficiency of bitcoin has received much attention recently we add to the literature by investigating nine forms of cryptocurrencies i e bitcoin ripple ethereum nem stellar litecoin dash monero and verge with a battery of efficiency tests and the empirical results indicate that all these cryptocurrencies are inefficient markets what is more we further construct a value weighted cryptocurrency composite index cci and show that cci and dow jones industrial average are persistently cross correlated

long memory of abnormal investor attention and the cross correlations between abnormal investor attention and trading volume volatility respectively abstract taking baidu index as a proxy for abnormal investor attention aia the long memory property in the aia of shanghai stock exchange sse 50 index component stocks was empirically investigated using detrended fluctuation analysis dfa method the results show that abnormal investor attention is power law correlated with hurst exponents between 0 64 and 0 98 furthermore the cross correlations between abnormal investor attention and trading volume volatility respectively are studied using detrended cross correlation analysis dcca and the dcca cross correlation coefficient d c c a the results suggest that there are positive correlations between aia and trading volume volatility respectively in addition the correlations for trading volume are in general higher than the ones for volatility by carrying on rescaled range analysis r s and rolling windows analysis we find that the results mentioned above are effective and significant

multifractal detrended cross correlation analysis of genome sequences using chaos game representation abstract we characterized the multifractal nature and power law cross correlation between any pair of genome sequence through an integrative approach combining 2d multifractal detrended cross correlation analysis and chaos game representation in this paper we have analyzed genomes of some prokaryotes and calculated fractal spectra h q and f from our analysis we observed existence of multifractal nature and power law cross correlation behavior between any pair of genome sequences cluster analysis was performed on the calculated scaling exponents to identify the class affiliation and the same is represented as a dendrogram we suggest this approach may find applications in next generation sequence analysis big data analytics etc

cross correlations between crude oil and agricultural commodity markets abstract in this paper we investigate cross correlations between crude oil and agricultural commodity markets based on a popular statistical test proposed by podobnik et al 2009 we find that the linear return cross correlations are significant at larger lag lengths and the volatility cross correlations are highly significant at all of the lag lengths under consideration using a detrended cross correlation analysis dcca we find that the return cross correlations are persistent for corn and soybean and anti persistent for oat and soybean the volatility cross correlations are strongly persistent using a nonlinear cross correlation measure our results show that cross correlations are relatively weak but they are significant for smaller time scales for larger time scales the cross correlations are not significant the reason may be that information transmission from crude oil market to agriculture markets can complete within a certain period of time finally based on multifractal extension of dcca we find that the cross correlations are multifractal and high oil prices partly contribute to food crisis during the period of 2006 mid 2008

preconditioning of periodic fast multipole method for solving volume integral equations application of fmm to the volume integral equations for pe riodic boundary value problem for maxwell s equations is investigated we propose effective preconditioners for the discretized volume integral equa tions we confirm the accuracy and high performances of the proposed method in several numerical examples including periodic problems near wood s anomalies developing fast solvers for periodic boundary value problems is an important research subject because they are the basis of the analysis for periodic structures such as metamaterials and photonic crystals inte gral equation methods accelerated with fmm fast multipole method 1 are considered to be among promising candidates for such solvers this is particularly true when the problem under consideration is of scattering type since integral equation methods can deal with radiation conditions easily in addition we can take the effect of the periodicity into account in fmm using a few additional ingredients including lat tice sums of the ordinary fundamental solution and its derivatives in this communication we focus on doubly periodic transmission problems for maxwell s equations an fmm accelerated boundary integral equation method biem for such problems called periodic fmm has been proposed by otani and nishimura 2 the periodic fmm uses the lattice sum expression of the periodic green s function to regard a periodic boundary value problem as an ordinary problem with an infinite repetition of the replicas of the unit cell contributions from far replica cells can then be evaluated as the local expansion of the level 0 cell and this expansion can be written with the lattice sums the lattice sums depend only on the periods wavenumber and the floquet wavenumber and can be precomputed once for all in the fmm algorithm otherwise the algorithm is exactly the same as the ordinary fmm which is a distinctive advantage of the periodic fmm in this communication we consider a volume integral equation counterpart of the periodic fmm 2 the volume integral equations are often used to analyze inhomogeneous media as we can see in 3 and 4 to mention just a few fast solvers for viem have also been investigated e g in 5 where the discrete bcg fft algorithm for the computation of the scattered fields from three dimensional inhomogeneous dielectric scatterers has been presented in 6 the adaptive integral method aim has been used to solve scattering problems for mixed dielectric and conducting objects with the help of volume surface integral equations in addition acceleration of viem

sad and stock returns revisited nonlinear analysis based on mf dcca and granger test abstract using both multifractal detrended fluctuation analysis mf dfa and multifractal detrended cross correlation analysis mf dcca methods this paper examines the effects of seasonal affective disorder sad on stock returns in the chinese stock market we first separate stocks into small and large cap stocks according to market value and find that sad and these two stock return series are multifractal based on mf dfa moreover we employ mf dcca and show that the cross correlation between sad and stock returns is anti persistent and the cross correlation between sad and return of small cap stock is stronger than that between sad and return of large cap stock furthermore we find that long range correlation is the main source of multifractality in the cross correlations between sad and small cap stock while both long range correlation and fat tails contribute to the multifractality between sad and large cap stock finally utilizing nonlinear granger causality test we find that sad is the granger cause of stock returns not vice versa

dcca cross correlation coefficient with sliding windows approach abstract we have implemented in this paper the dcca cross correlation coefficient d c c a with sliding windows approach to measure cross correlation as a function of time thereby from two non stationary time series a fixed size window will be defined by w and the coefficients d c c a calculated for different time scales 4 n w 4 following this procedure it was possible to set up a cross correlation color map of d c c a which was tested in this paper for a simulated random and an empirical econometric case this new color map with sliding windows shows the time scale and the current time for a cross correlation analysis in this way it is possible to analyze the complete cross correlation between two variables in different areas of knowledge such as economics our case of study

china s soybean crush spread nonlinear analysis based on mf dcca abstract in this paper we investigate cross correlations among the soybean soymeal and soyoil futures return series at china s dalian commodity exchange using both multifractal detrended fluctuation analysis mf dfa and multifractal detrended cross correlation analysis mf dcca methods we find that there exists multifractality in the soybean soymeal and soyoil futures return series using mf dfa method moreover results of mf dcca demonstrate that there exists a power law cross correlation and significant multifractal features among soybean soymeal and soyoil futures returns furthermore by comparing the multifractality of the original series to the shuffled and surrogated series we find that both the long range correlations and fat tailed distribution contribute to the multifractality in cross correlation among soybean soymeal and soyoil futures return series in dalian commodity exchanges furthermore we use nonlinear granger causality test and show that anyone of the three products returns is the granger cause of the other two products returns

dynamical mechanism in aero engine gas path system using minimum spanning tree and detrended cross correlation analysis abstract identifying the mutual interaction in aero engine gas path system is a crucial problem that facilitates the understanding of emerging structures in complex system by employing the multiscale multifractal detrended cross correlation analysis method to aero engine gas path system the cross correlation characteristics between gas path system parameters are established further we apply multiscale multifractal detrended cross correlation distance matrix and minimum spanning tree to investigate the mutual interactions of gas path variables the results can infer that the low spool rotor speed n1 and engine pressure ratio epr are main gas path parameters the application of proposed method contributes to promote our understanding of the internal mechanisms and structures of aero engine dynamics

fuzzy entropy complexity and multifractal behavior of statistical physics financial dynamics abstract the present paper introduces a novel stochastic statistical physics financial system by combining exclusion system with compound poisson process the financial system is a model concerning with price fluctuations derived from dissemination of trading attitudes of financial agents and random drastic jumps caused by exogenous economic factors respectively multifractal detrended cross correlation analysis and fuzzy entropy of simulation dataset are performed fractional fuzzy entropy based on fuzzy entropy method and fractional calculus is also developed to explore nonlinear complexity of the proposed model in the paper empirical results display that there are significant multifractality and fractal asymmetry in the proposed model and the complexity of return series increases when or increases correspondingly and same analyses for actual stock market dataset are comparatively investigated the conclusions reveal that the novel compound dynamics can reproduce various nonlinear behaviors of actual stock markets to a certain extent

an analysis of the intrinsic cross correlations between api and meteorological elements using dpcca abstract in order to reveal the intrinsic cross correlations between air pollution index api records and synchronously meteorological elements data the detrended partial cross correlation dpcc coefficients are analyzed using a detrended partial cross correlation analysis dpcca dpcc coefficients for different spatial locations and seasons are calculated and compared the results show that dpcca can uncover intrinsic cross correlations between api and meteorological elements and most of their interactional mechanisms can be explained dpcc coefficients are either positive or negative and vary with spatial locations and seasons with consistently interactional mechanisms more remarkable we find that detrended cross correlation analysis can present the cross correlations between the fluctuations in two nonstationary time series but this cross correlation does not always fully reflect the interactional mechanism for the original time series despite this dpcca is recommended as a comparatively reliable method for revealing intrinsic cross correlations between api and meteorological elements and it can also be useful for our understanding of their interactional mechanisms

on volume integral equations the focus of this paper is on the volume integral representations to be used in constructing integral equations for composite volume media the major thrust of the paper is to identify where derivatives of a discontinuous function arise in the derivation of the volume representation three different derivation methods are presented resulting in identical representation independent of the derivation method these representations agree with some in the existing literature and disagree with others when an electric field formulation is considered the source of disagreement manifests itself only when magnetic materials are present likewise for the dual situation the inconsistency appears for a magnetic field formulation of dielectric materials this paper identifies the sources of error in the incorrect representations and its major contribution is the rigorously correct derivation of the representations to be used in volume integral equations we also present numerical results for an integral equation derived from our representation the numerical results employ only the e field as the unknown and the singularity is handled in a manner analogous to a standard numerical treatment of the electric field integral equation

assessment of 48 stock markets using adaptive multifractal approach abstract in this paper stock market comovements are examined using cointegration granger causality tests and nonlinear approaches in context of mutual information and correlations since underlying data sets are affected by non stationarities and trends we also apply adaptive multifractal detrended fluctuation analysis amf dfa and adaptive multifractal detrended cross correlation analysis amf dxa we find only 170 pair of stock markets cointegrated and according to the granger causality and mutual information we realize that the strongest relations lies between emerging markets and between emerging and frontier markets according to scaling exponent given by amf dfa h q 2 1 we find that all underlying data sets belong to non stationary process according to efficient market hypothesis emh only 8 markets are classified in uncorrelated processes at 2 confidence interval 6 stock markets belong to anti correlated class and dominant part of markets has memory in corresponding daily index prices during january 1995 to february 2014 new zealand with h 0 457 0 004 and jordan with h 0 602 0 006 are far from emh the nature of cross correlation exponents based on amf dxa is almost multifractal for all pair of stock markets the empirical relation h x y h x x h y y 2 is confirmed mentioned relation for q 0 is also satisfied while for q 0 there is a deviation from this relation confirming behavior of markets for small fluctuations is affected by contribution of major pair for larger fluctuations the cross correlation contains information from both local internal and global external conditions width of singularity spectrum for auto correlation and cross correlation are x x 0 304 0 905 and x y 0 246 1 178 respectively the wide range of singularity spectrum for cross correlation confirms that the bilateral relation between stock markets is more complex the value of d c c a indicates that all pairs of stock market studied in this time interval belong to cross correlated processes

the informativeness of options trading activities non linear analysis based on mf dcca and granger test abstract using both put call ratio p c and option to stock volume ratio o s as measures of options trading activities this paper investigates the information content of sse 50 etf options we begin by using detrended fluctuation analysis mf dfa method to examine the multifractal properties of p c ratio change series o s ratio change series and etf returns and find that these time series exhibit strong multifractality moreover employing multifractal detrended cross correlation analysis mf dcca we show that there exists strong anti persistent cross correlation between p c ratios and 50 etf returns but no significant cross correlation between o s ratios and 50 etf returns suggesting that p c ratio is more pronounced detecting the information content of options furthermore we use nonlinear granger causality test and show that the granger causality relationships between p c ratio and 50 etf return series is more significant and stronger than that between o s ratio and 50 etf return series our findings imply that p c ratio can better capture the informativeness of options

active appearance models revisited active appearance models aams and the closely related concepts of morphable models and active blobs are generative models of a certain visual phenomenon although linear in both shape and appearance overall aams are nonlinear parametric models in terms of the pixel intensities fitting an aam to an image consists of minimising the error between the input image and the closest model instances i e solving a nonlinear optimisation problem we propose an efficient fitting algorithm for aams based on the inverse compositional image alignment algorithm we show that the effects of appearance variation during fitting can be precomputed projected out using this algorithm and how it can be extended to include a global shape normalising warp typically a 2d similarity transformation we evaluate our algorithm to determine which of its novel aspects improve aam fitting performance

long range correlation analysis among non stationary passive scalar series in the turbulent boundary layer abstract in this paper we investigated the cross correlations among non stationary passive scalars series based on the detrended cross correlation analysis dcca we found that there existed long range cross correlations among these series the dcca exponents for each pair of passive scalars are all greater than 0 5 this implies that the cross correlation is persistent moreover the degree of cross correlation between h 2 o and pm 2 5 is greater compared to co2 and pm 2 5 as well as co2 and h 2 o the same analysis is also performed on randomly shuffled series the calculated dcca exponents fluctuate slightly over 0 5 which verifies that there indeed exist cross correlations among original series

volatility aggregation intensity energy futures series on stochastic finite range exclusion dynamics abstract the stochastic finite range exclusion process one of statistical physics systems is introduced to construct a new agent based financial price model to study the mechanism of market dynamics a novel volatility aggregation intensity vai time series describing the fluctuation aggregation intensity of the volatility series is developed for further investigating the nonlinear volatility behaviors in energy markets for studying the proposed vai series and the proposed price model the new york mercantile exchange energy futures data is selected and analyzed further cross correlation analysis auto correlation analysis with multiscale and multifractal detrended fluctuation analysis are applied to analyze the correlation volatility clustering and multifractal natures of the vai time series the empirical results show that the proposed model has the parallel behaviors with the authentic markets indicating that it is rational and available the new concept of vai series is of great value and can enrich the study of volatility behaviors in energy markets to some extent

a discontinuous galerkin volume integral equation method for scattering from inhomogeneous objects a discontinuous galerkin volume integral equation dgvie method is proposed for scattering from inhomogeneous objects it is shown that this dgvie has several advantages over other volume integral methods first the dgvie can be used for nonconformal meshes second the order of the singularity in dgvie has been reduced from 1 r text 3 to 1 r and the implementation of dgvie becomes simple third the proposed dgvie converges faster than the traditional vie methods especially for objects with high permittivity numerical results validate the accuracy and efficiency of the proposed dgvie

meshfree solutions of volume integral equations for electromagnetic scattering by anisotropic objects volume integral equations vies are indispensable for solving inhomogeneous or anisotropic electromagnetic em problems by integral equation approach the solution of vies strongly relies on the discretization of volume integral domains and tetrahedral elements in discretization are usually preferred for arbitrary geometric shapes unlike discretizing a surface domain discretizing a volume domain is very inconvenient in practice and special commercial software is needed in general even for a simple and regular geometry to release the burden of descretizing volume domains especially to remove the constraint of mesh conformity in the traditional method of moments mom we propose a novel meshfree scheme for solving vies in this paper the scheme is based on the transformation of volume integrals into boundary integrals through the green gauss theorem when integral kernels are regularized by excluding a small cylinder enclosing the observation node the original integral domain represented by the object is also expanded to a cylindrical domain circumscribing the object to facilitate the evaluation of boundary integrals the singular integrals over the small cylinder are specially handled with singularity subtraction techniques numerical examples for em scattering by inhomogeneous or anisotropic objects are presented to illustrate the scheme and good results are observed

direct determination approach for the multifractal detrending moving average analysis in the canonical framework we propose an alternative approach for the multifractal analysis based on the detrending moving average method mf dma we define a canonical measure such that the multifractal mass exponent q is related to the partition function and the multifractal spectrum f can be directly determined the performances of the direct determination approach and the traditional approach of the mf dma are compared based on three synthetic multifractal and monofractal measures generated from the one dimensional p model the two dimensional p model and the fractional brownian motions we find that both approaches have comparable performances to unveil the fractal and multifractal nature in other words without loss of accuracy the multifractal spectrum f can be directly determined using the new approach with less computation cost we also apply the new mf dma approach to the volatility time series of stock prices and confirm the presence of multifractality

detrended partial cross correlation analysis of two nonstationary time series influenced by common external forces when common factors strongly influence two power law cross correlated time series recorded in complex natural or social systems using detrended cross correlation analysis dcca without considering these common factors will bias the results we use detrended partial cross correlation analysis dpxa to uncover the intrinsic power law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces the dpxa method is a generalization of the detrended cross correlation analysis that takes into account partial correlation analysis we demonstrate the method by using bivariate fractional brownian motions contaminated with a fractional brownian motion we find that the dpxa is able to recover the analytical cross hurst indices and thus the multiscale dpxa coefficients are a viable alternative to the conventional cross correlation coefficient we demonstrate the advantage of the dpxa coefficients over the dcca coefficients by analyzing contaminated bivariate fractional brownian motions we calculate the dpxa coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the u s dollar index we develop the multifractal dpxa mf dpxa method in order to generalize the dpxa method and investigate multifractal time series we analyze multifractal binomial measures masked with strong white noises and find that the mf dpxa method quantifies the hidden multifractal nature while the multifractal dcca method fails

power law correlations in finance related google searches and their cross correlations with volatility and traded volume evidence from the dow jones industrial components abstract we study power law correlations properties of the google search queries for dow jones industrial average djia component stocks examining the daily data of the searched terms with a combination of the rescaled range and rescaled variance tests together with the detrended fluctuation analysis we show that the searches are in fact power law correlated with hurst exponents between 0 8 and 1 1 the general interest in the djia stocks is thus strongly persistent we further reinvestigate the cross correlation structure between the searches traded volume and volatility of the component stocks using the detrended cross correlation and detrending moving average cross correlation coefficients contrary to the universal power law correlations structure of the related google searches the results suggest that there is no universal relationship between the online search queries and the analyzed financial measures even though we confirm positive correlation for a majority of pairs there are several pairs with insignificant or even negative correlations in addition the correlations vary quite strongly across scales

detrended moving average partial cross correlation analysis on financial time series abstract cross correlations between nonlinear time series widely exist in complex systems it is of great importance to accurately measure the correlations between time series in this work we suggest a combination methodology of detrended moving average processing and partial cross correlation analysis to quantify the correlations between different time series which we call as detrended moving average partial cross correlation analysis d m p c c a this novel approach combines the advantages of detrended moving average processing and partial cross correlation analysis not only exploring power law cross correlations between two signals but removing underlying impacts of other signals on those two signals to demonstrate the advantages of this approach we carry out experiments with synthetic data generated by correlated processes and compare the performance of this measurement to traditional cross correlation techniques it is found that this method can reveal the real cross correlations between systems even when the indirect cross correlations established by other common factors exist then we go further study into the application of d m p c c a to financial time series in order to report its performance in stock markets and investigate cross correlations between different stock indices furthermore the rolling windows method is used in conjunction with d m p c c a to capture the changes of cross correlations between stock indices as time goes on we notice that there is a special period when d m p c c a coefficients between stock indices are obviously different from those of other periods

analysis of volume integral equation formulations for scattering by high contrast penetrable objects the volume integral equation method is applied in electromagnetic scattering from arbitrarily shaped three dimensional inhomogeneous objects the properties of the volume electric and magnetic field integral equations vefie and vmfie are investigated numerical experiments show that if the galerkin s method with the lowest mixed order basis functions is used to discretize the equations the accuracy of the vmfie can be significantly poorer than the accuracy of the vefie in particular for high contrast objects at high frequencies the accuracy of the vmfie can be essentially improved with full first order linear basis functions the linear basis functions are found to be useful also when a single volume integral equation is used to model a general scatterer where both permittivity and permeability differ from the background

how long the singular value decomposed entropy predicts the stock market evidence from the dow jones industrial average index in this paper a new concept of multi scales singular value decomposition entropy based on dcca cross correlation analysis is proposed and its predictive power for the dow jones industrial average index is studied using granger causality analysis with different time scales it is found that the singular value decomposition entropy has predictive power for the dow jones industrial average index for period less than one month but not for more than one month this shows how long the singular value decomposition entropy predicts the stock market that extends caraiani s result obtained in caraiani 2014 on the other hand the result also shows an essential characteristic of stock market as a chaotic dynamic system

multifractal analysis of the wti crude oil market us stock market and epu abstract this paper adopts multifractal methods to analyze the nonlinear correlations among economic policy uncertainty epu the crude oil market and the stock market first using multifractal detrended fluctuation analysis mf dfa we find that each of the three series shows multifractality and the sources of multifractality are all from long range correlations and fat tailed distributions however for the stock and oil price series the long range correlation has a greater impact while the fat tailed distribution contributes more to epu multifractality furthermore we apply multifractal detrended cross correlation analysis mf dcca to analyze the cross correlation multifractal features among the three series the generalized hurst exponents of any two series are significantly greater than 0 5 and the stock and oil price series have the strongest cross correlation values coupling detrended fluctuation cdfa analysis of the three series shows that the coupling correlation among the three sequences is also multifractal the chi squared statistic reveals that the contribution of the stock market to the multifractality of the coupling correlation is greater than that of other series and epu has the smallest influence on the coupling correlation finally we employ multiscale multifractal analysis mma to visualize the dynamic behaviors of the correlations among the series the results show that the cross correlation of stock with the oil price has some symmetry at small and large scale fluctuations additionally the influence of epu on the overall coupling features is mainly reflected under small scale fluctuations and at large scales the coupling correlation of the three series remains similar to the hurst surface of stock with the oil price these results are useful for building predictive models

lucas kanade 20 years on a unifying framework since the lucas kanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision applications range from optical flow and tracking to layered motion mosaic construction and face coding numerous algorithms have been proposed and a wide variety of extensions have been made to the original formulation we present an overview of image alignment describing most of the algorithms and their extensions in a consistent framework we concentrate on the inverse compositional algorithm an efficient algorithm that we recently proposed we examine which of the extensions to lucas kanade can be used with the inverse compositional algorithm without any significant loss of efficiency and which cannot in this paper part 1 in a series of papers we cover the quantity approximated the warp update rule and the gradient descent approximation in future papers we will cover the choice of the error function how to allow linear appearance variation and how to impose priors on the parameters

a comparative study of two dimensional multifractal detrended fluctuation analysis and two dimensional multifractal detrended moving average algorithm to estimate the multifractal spectrum multifractal detrended fluctuation analysis mfdfa and multifractal detrended moving average mfdma algorithm have been established as two important methods to estimate the multifractal spectrum of the one dimensional random fractal signal they have been generalized to deal with two dimensional and higher dimensional fractal signals this paper gives a brief introduction of the two dimensional multifractal detrended fluctuation analysis 2d mfdfa and two dimensional multifractal detrended moving average 2d mfdma algorithm and a detailed description of the application of the two dimensional fractal signal processing by using the two methods by applying the 2d mfdfa and 2d mfdma to the series generated from the two dimensional multiplicative cascading process we systematically do the comparative analysis to get the advantages disadvantages and the applicabilities of the two algorithms for the first time from six aspects such as the similarities and differences of the algorithm models the statistical accuracy the sensitivities of the sample size the selection of scaling range the choice of the q orders and the calculation amount the results provide a valuable reference on how to choose the algorithm from 2d mfdfa and 2d mfdma and how to make the schemes of the parameter settings of the two algorithms when dealing with specific signals in practical applications

theory and practice of hierarchical data driven descent for optimal deformation estimation real world surfaces such as clothing water and human body deform in complex ways estimating deformation parameters accurately and reliably is hard due to its high dimensional and non convex nature optimization based approaches require good initialization while regression based approaches need a large amount of training data recently to achieve globally optimal estimation data driven descent tian and narasimhan in int j comput vis 98 279 302 2012 applies nearest neighbor estimators trained on a particular distribution of training samples to obtain a globally optimal and dense deformation field between a template and a distorted image in this work we develop a hierarchical structure that first applies nearest neighbor estimators on the entire image iteratively to obtain a rough estimation and then applies estimators with local image support to refine the estimation compared to its non hierarchical version our approach has the theoretical guarantees with significantly fewer training samples is faster by several orders provides a better metric deciding whether a given image requires more or fewer samples and can handle more complex scenes that include a mixture of global motion and local deformation we demonstrate in both simulation and real experiments that the proposed algorithm successfully tracks a broad range of non rigid scenes including water clothing and medical images and compares favorably against several other deformation estimation and tracking approaches that do not provide optimality guarantees

coupling detrended fluctuation analysis of the relationship between pm2 5 concentration and weather elements abstract pm2 5 pollution in china is getting worse the relationship between pm2 5 concentration and weather elements exhibits complex non linear feature therefore it is very important to study the sophisticated mathematical and nonlinear method about the relationship between pm2 5 concentration and weather elements coupling detrended fluctuation analysis cdfa method can reveal the multifractal properties of more than two series the coupled correlations between pm2 5 concentration and weather elements in hangzhou and guangzhou are explored by cdfa we find that coupled correlations are not only multifractal but also anti persistent compared with guangzhou hangzhou has stronger correlations both long range correlation and fat tailed distribution can cause the coupled multifractality and the long range correlation has a greater influence on the coupled multifractality in addition the coupling sensitivity degree csd y is used to study which weather element plays a key role in the change of pm2 5 concentration

detrended multiple cross correlation coefficient abstract based on the detrended cross correlation coefficient x a x b we propose in this paper to establish a detrended multiple cross correlation coefficient d m c x 2 to be applied in order to quantify the relations between non stationary signals on different time scales this detrended multiple cross correlation coefficient will be the measure of the total association between the dependent variable y with the others i independent variables x it will be the generalization of x a x b for more than two independent variables to validate d m c x 2 we set up a cross correlation table ranging from 0 to 1 and we determined the detrended multiple cross correlation coefficient into simulated and real time series the results of this analysis show that the detrended multiple cross correlation coefficient may be suitable and useful to measure multiple cross correlations as we will see in this paper

recurrence duration statistics and time dependent intrinsic correlation analysis of trading volumes a study of chinese stock indices abstract the trading volume in stock markets is known as an important variable which reflects the liquidity of the financial markets and therefore is regarded to be greatly important for the measurement of market liquidity risk in this work a new concept called recurrence duration is introduced for study of daily trading volumes which is inspired by idea of the volatility duration that was proposed and studied in our previous work the recurrence duration is thought as the shortest passing time that the following days trading volume takes to exceed or go below the current trading volume which is time varying similar to the volatility duration distribution of the price returns the power law function could describe the empirical probability distribution of recurrence durations of trading volumes and their tail distributions can be fitted by two stretched exponential functions further the correlation relationships of trading volumes between chinese stock indices as well as the correlations of recurrence durations are investigated one approach employed is a recently proposed method time dependent intrinsic correlation tdic which is based on the empirical mode decomposition emd to decompose nonlinear and nonstationary signals into the intrinsic mode functions imfs the instantaneous periods of which are used then in determination of the sizes of sliding windows to compute the running correlation coefficients for the multiscale signals the empirical results reveal rich patterns of correlations for both trading volumes and recurrence durations at different scales for different modes another approach is the widely used dcca cross correlation coefficient by which the level of cross correlation is measured for both original series and imf modes of the stock indices

backcasting and forecasting time series using detrended cross correlation analysis abstract in this paper we addressed the problem of backcasting and forecasting non stationary time series given that traditional backcasting are based on the classical cross correlation function assuming two stationary time series assumptions based on two non stationary time series need a new approach thus detrended cross correlation analysis dcca provides a fanned estimator for a correlation between two non stationary time series providing then the stationary time series therefore we developed a backcaster estimator and a predictor for the obtained stationary time series the practical example of a chilean cement production time series 1991 2015 illustrates our results

globally optimal estimation of nonrigid image distortion image alignment in the presence of non rigid distortions is a challenging task typically this involves estimating the parameters of a dense deformation field that warps a distorted image back to its undistorted template generative approaches based on parameter optimization such as lucas kanade can get trapped within local minima on the other hand discriminative approaches like nearest neighbor require a large number of training samples that grows exponentially with respect to the dimension of the parameter space and polynomially with the desired accuracy 1 in this work we develop a novel data driven iterative algorithm that combines the best of both generative and discriminative approaches for this we introduce the notion of a pull back operation that enables us to predict the parameters of the test image using training samples that are not in its neighborhood not close in the parameter space we prove that our algorithm converges to the global optimum using a significantly lower number of training samples that grows only logarithmically with the desired accuracy we analyze the behavior of our algorithm extensively using synthetic data and demonstrate successful results on experiments with complex deformations due to water and clothing