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Random packing of spheres inside fractal collectors of dimension 2 < d < 3 is studied numerically using Random Sequential Adsorption (RSA) algorithm. The paper focuses mainly on the measurement of random packing saturation limit. Additionally, scaling properties of density autocorrelations in the obtained packing are analyzed. The RSA kinetics coefficients are also measured. Obtained results allow to test phenomenological relation between random packing saturation density and collector dimension. Additionally, performed simulations together with previously obtained results confirm that, in general, the known dimensional relations are obeyed by systems having non-integer dimension, at least for d < 3. [ABSTRACT FROM AUTHOR]

The dynamical properties of dipolar Janus particles are studied through simulation using our previously-developed detailed pointwise (PW) model and an isotropically coarse-grained (CG) model [M. C. Hagy and R. Hernandez, J. Chem. Phys. 137, 044505 (2012)]. The CG model is found to have accelerated dynamics relative to the PW model over a range of conditions for which both models have near identical static equilibrium properties. Physically, this suggests dipolar Janus particles have slower transport properties (such as diffusion) in comparison to isotropically attractive particles. Time rescaling and damping with Langevin friction are explored to map the dynamics of the CG model to that of the PW model. Both methods map the diffusion constant successfully and improve the velocity autocorrelation function and the mean squared displacement of the CG model. Neither method improves the distribution of reversible bond durations f(tb) observed in the CG model, which is found to lack the longer duration reversible bonds observed in the PW model. We attribute these differences in f(tb) to changes in the energetics of multiple rearrangement mechanisms. This suggests a need for new methods that map the coarse-grained dynamics of such systems to the true time scale. [ABSTRACT FROM AUTHOR]

Aiming at the problem of large amount of calculation and long recognition time of traditional method of modulation identification of centralised multiple-input multiple-output (MIMO) radar signal, partial zero-delay instantaneous autocorrelation spectrum is proposed to identify the modulation of centralised MIMO radar here. In this method, the modulation identification of the centralised MIMO radar is performed by extracting the ratio of the sub-peak to the highest peak in the zero-delay transient autocorrelation spectrum of the part signal. The computational complexity of this method is low, and it identifies without additional features for coded MIMO radar signals. Here, the definition of partial instantaneous correlation spectrum is given; the feasibility of using this method to identify the modulation type of the centralised MIMO radar is analysed, and the identification process is discussed. It is showed that the proposed method can shorten the recognition time while guaranteeing the detection performance under a certain SNR in the simulation results. [ABSTRACT FROM AUTHOR]

Terahertz (THz) active normal-mode relaxation in crystalline pentaerythritol tetranitrate (PETN) was studied using classical molecular dynamics simulations for energy and density conditions corresponding to room temperature and atmospheric pressure. Two modifications to the fully flexible non-reactive force field due to Borodin et al. [J. Phys. Chem. B 112, 734 (2008)] used in a previous study of THz-active normal-mode relaxation in PETN [J. Chem. Phys. 134, 014513 (2011)] were considered to assess the sensitivity of the earlier predictions to details of the covalent bond-stretching terms in the force field. In the first modification the harmonic bond-stretching potential was replaced with the Morse potential to study the effect of bond anharmonicity on the THz-region mode relaxation. In the second modification the C-H and nitro-group N-O bond lengths were constrained to constant values to mimic lower quantum occupation numbers for those high-frequency modes. The results for relaxation times of the initially excited modes were found to be insensitive to either force-field modification. Overall time scales for energy transfer to other modes in the system were essentially unaffected by the force-field modifications, whereas the detailed pathways by which the energy transfer occurs are more complicated for the Morse potential than for the harmonic-bond and fixed-bond cases. Terahertz infrared absorption spectra constructed using calculated normal-mode frequencies, transition dipoles, and relaxation times for THz-active modes were compared to the spectra obtained from the Fourier transform of the dipole-dipole time autocorrelation function (DDACF). Results from the two approaches are in near agreement with each other and with experimental results in terms of main peak positions. Both theoretical methods yield narrower peaks than observed experimentally and in addition predict a weaker peak at ω ∼ 50 cm-1 that is weak or absent experimentally. Peaks obtained using the DDACF approach are broader than those obtained from the normal-mode relaxation method. [ABSTRACT FROM AUTHOR]

On the mesoscale, the molecular motion in a microporous material can be represented as a sequence of hops between different pore locations and from one pore to the other. On the same scale, the memory effects in the motion of a tagged particle are embedded in the displacement autocorrelation function (DACF), the discrete counterpart of the velocity autocorrelation function (VACF). In this paper, a mesoscopic hopping model, based on a lattice-gas automata dynamics, is presented for the coarse-grained modeling of the DACF in a microporous material under conditions of thermodynamic equilibrium. In our model, that we will refer to as central cell model, the motion of one tagged particle is mimicked through probabilistic hops from one location to the other in a small lattice of cells where all the other particles are indistinguishable; the cells closest to the one containing the tagged particle are simulated explicitly in the canonical ensemble, whereas the border cells are treated as mean-field cells in the grand-canonical ensemble. In the present paper, numerical simulation of the central cell model are shown to provide the same results as a traditional lattice-gas simulation. Along with this a mean-field theory of self-diffusion which incorporates time correlations is discussed. [ABSTRACT FROM AUTHOR]

A new method, here called thermal Gaussian molecular dynamics (TGMD), for simulating the dynamics of quantum many-body systems has recently been introduced [I. Georgescu and V. A. Mandelshtam, Phys. Rev. B 82, 094305 (2010)]. As in the centroid molecular dynamics (CMD), in TGMD the N-body quantum system is mapped to an N-body classical system. The associated both effective Hamiltonian and effective force are computed within the variational Gaussian wave-packet approximation. The TGMD is exact for the high-temperature limit, accurate for short times, and preserves the quantum canonical distribution. For a harmonic potential and any form of operator A⁁, it provides exact time correlation functions CAB(t) at least for the case of B⁁, a linear combination of the position, x⁁, and momentum, p⁁, operators. While conceptually similar to CMD and other quantum molecular dynamics approaches, the great advantage of TGMD is its computational efficiency. We introduce the many-body implementation and demonstrate it on the benchmark problem of calculating the velocity time auto-correlation function for liquid para-hydrogen, using a system of up to N = 2592 particles. [ABSTRACT FROM AUTHOR]

The coarse-graining of a simple all-atom 2D microscopic model of graphene, in terms of 'blobs' described by center of mass variables, is presented. The equations of motion of the coarse-grained variables take the form of dissipative particle dynamics (DPD). The coarse-grained conservative forces and the friction of the DPD model are obtained via a bottom-up procedure from molecular dynamics (MD) simulations. The separation of timescales for blobs of 24 and 96 carbon atoms is sufficiently pronounced for the Markovian assumption, inherent to the DPD model, to provide satisfactory results. In particular, the MD velocity autocorrelation function of the blobs is well reproduced by the DPD model, provided that the effect of friction and noise is taken into account. However, DPD cross-correlations between neighbor blobs show appreciable discrepancies with respect to the MD results. Possible extensions to mend these discrepancies are briefly outlined. [ABSTRACT FROM AUTHOR]

In this article, we describe coupled coherent states (CCS) simulations of vibrational predissociation of weakly bounded complexes. The CCS method is implemented in the Cartesian frame in a manner that is similar to classical molecular dynamics. The calculated lifetimes of the vibrationally excited Ne-Br2(ν) complexes agree with experiment and previous calculations. Although the CCS method is, in principle, a fully quantum approach, in practice it typically becomes a semiclassical technique at long times. This is especially true following dissociation events. Consequently, it is very difficult to converge the quantum calculations of the final Br2 vibrational distributions after predissociation and of the autocorrelation functions. However, the main advantage of the method is that it can be applied with relative ease to determine the lifetimes of larger complexes and, in order to demonstrate this, preliminary results for tetra- and penta-atomic clusters are reported. [ABSTRACT FROM AUTHOR]

This paper presents comparisons between molecular dynamics simulations (MDSs) and the Keilson and Storer (KS) model for collision-induced translational velocity changes in pure H2 at room temperature from four different points of view. The first considers various autocorrelation functions associated with the velocity. The second and third comparisons are made for the collision kernels and for the time evolutions of some conditional probabilities for changes in the velocity modulus and orientation. Finally, the evolutions, with density, of the half widths of the Q(1) lines of the isotropic Raman (1–0) fundamental band and of the (2–0) overtone quadrupole band are investigated. The results demonstrate that, while the KS approach gives a poor description of detailed velocity-to-velocity changes, it leads to accurate results for the correlation functions and spectral shapes, quantities resulting from large averages over the velocity. On the opposite, collision kernels derived from MDS lead to accurate predictions of all considered quantities. The results open promising perspectives for modeling of the spectral shapes of other systems. They also stress the value of direct calculations of speed-dependent broadening and shifting parameters from the intermolecular potential to avoid their determination from measured spectra and permit fully meaningful tests of the models. [ABSTRACT FROM AUTHOR]

This paper presents comparisons between molecular dynamics simulations (MDSs) and the Keilson and Storer (KS) model for collision-induced translational velocity changes in pure H2 at room temperature from four different points of view. The first considers various autocorrelation functions associated with the velocity. The second and third comparisons are made for the collision kernels and for the time evolutions of some conditional probabilities for changes in the velocity modulus and orientation. Finally, the evolutions, with density, of the half widths of the Q(1) lines of the isotropic Raman (1–0) fundamental band and of the (2–0) overtone quadrupole band are investigated. The results demonstrate that, while the KS approach gives a poor description of detailed velocity-to-velocity changes, it leads to accurate results for the correlation functions and spectral shapes, quantities resulting from large averages over the velocity. On the opposite, collision kernels derived from MDS lead to accurate predictions of all considered quantities. The results open promising perspectives for modeling of the spectral shapes of other systems. They also stress the value of direct calculations of speed-dependent broadening and shifting parameters from the intermolecular potential to avoid their determination from measured spectra and permit fully meaningful tests of the models. [ABSTRACT FROM AUTHOR]

We present results from molecular dynamics simulations for the anisotropic self-diffusion tensor and the velocity autocorrelation functions of monodisperse systems of dense linear chain molecules under flow. Two molecular models are used in these simulations: The finitely extensible nonlinear elastic chain and the freely jointed tangent sphere chain. Nonequilibrium molecular dynamics is used to simulate these systems under planar Couette flow and planar extensional flow. Under planar extensional flow, results presented here are the first, from simulation, for diffusion and velocity autocorrelation functions of molecules, while for planar Couette flow, we compare the broadest range of conditions. An explicit derivation is provided of the Green–Kubo expression for the diffusion tensor. This expression is then used to derive the relation involving the mean-squared displacement—an often used alternative method to calculate diffusion coefficients. Velocity autocorrelation functions have been used, in combination with results on the alignment of molecules from a previous paper, to provide some details of the molecular scale dynamics that influence diffusive transport under flow. [ABSTRACT FROM AUTHOR]

We explore how the width of the three-pulse photon echo signal at long population times can be used to determine the strength of the system-bath interaction. Using simulation with finite pulses we show that a simple relation exist between the width of the echo signal, the coupling strength, and the pulse autocorrelation. The derived model is applied to Rhodamine 6G in alcoholic solution, a paradigm system for the study of solvation dynamics, and the results are compared to conventional methods. The independently determined coupling strength forms the basis for a computationally inexpensive route to determine the entire spectral density, the key parameter when considering system-bath interactions. Our method allows us to accurately estimate the relative amplitude of fast and slow components in the correlation function using only impulsive limit simulations. We show that the peak shift significantly overestimates the amplitude of the fastest decay component for our experimental data. Changing solvent from methanol to 1-hexanol we observe a narrowing of the width of the echo profile. The changes in the echo width allow us to estimate the changes of the coupling strength in various solvents. [ABSTRACT FROM AUTHOR]

In a previous paper [G. Rickayzen and D. M. Heyes, J. Chem. Phys. 126, 234503 (2007)] we investigated by theory and molecular dynamics (MD) simulation the force and velocity autocorrelation functions of a fluid of molecules interacting through steeply varying potentials of the form [lowercase_phi_synonym](r)=4[variant_greek_epsilon][z2(r)-z(r)], where z(r)=(σ/r)n, [variant_greek_epsilon] and σ set the energy and length scale of the interaction, respectively, and n is an adjustable exponent (n=72 and 144 were considered in that work). Discrepancies between the theory and simulation were found except at the shortest times for some of the state points. In order to identify the origin(s) of these discrepancies, we have investigated here another fluid, in which the particles interact via the Morse potential in which z(r)=exp(-κ(r-σ)/σ), where κ is now the “steepness” parameter. The parameter κ is the analog of n, and this potential form is used in order to compare with previous results and establish better the origin of the differences between theory and simulation. It is shown in a further development of the theory that the actual form of the potential in the steep and short-ranged attractive limit is immaterial, and there exists a law of corresponding states for such potentials. This conclusion is confirmed by the MD simulations with the two potential forms for κ=n=144. The difference between the theory and simulation correlation functions increases with density, and it is concluded that these differences probably originate in many-body effects in time, which are absent in the theory. For packing fractions below about 0.2 the agreement between the theory and MD simulation force and velocity autocorrelation functions is nevertheless very good at all accessible times. [ABSTRACT FROM AUTHOR]

A new quasiclassical method for quantum autocorrelation functions based on the semiclassical limit in Wigner phase space has been derived. Unlike the existing quasiclassical method, the new method enables long-time simulations, thus making it possible to locate quantum spectral lines very precisely. The new method has been tested for a one-dimensional anharmonic oscillator fitted to the H2 molecule and for a six-dimensional calculation of the Ar2I van der Waals cluster in adiabatic approximation. The obtained results compare well with the benchmark quantum-mechanical calculations and are also roughly comparable to the experimental Ar2I- zero-kinetic-energy photoelectron spectrum, which is available in the literature. [ABSTRACT FROM AUTHOR]

We describe a dynamical approach to thermal regulation in molecular dynamics. Temperature is moderated by a control law and an additional variable, as in Nosé dynamics, but whose influence on the system decreases as the system approaches equilibrium. This device enables approximation of microcanonical averages and autocorrelation functions consistent with a given target temperature. Moreover, we demonstrate that the suggested technique is effective for the control of heat dissipation in a nonequilibrium setting, first by showing that the temperature control correctly regulates heat introduced by a rapid change to the system, and then by studying the slow relaxation of vibrational degrees of freedom (e.g., due to bonded atoms) in a solvent bath. [ABSTRACT FROM AUTHOR]

We propose a model for the short-time dynamics of fluids confined in slit-shaped pores. The model has been developed from the observation that the real lobe of the instantaneous normal mode density of states (INM DOS) closely follows a γ distribution. By proposing that the density of states of the confined fluid can be represented by a γ distribution, the resulting velocity autocorrelation function (VACF) is constructed such that it is accurate upto the fourth frequency moment. The proposed model results in an analytical expression for the VACF and relaxation times. The VACFs obtained from the model have been compared with the VACFs obtained from molecular dynamic simulations and INM analysis for fluids confined in slit-shaped pores over a wide range of confinement and temperatures. The model is seen to capture the short-time behavior of the VACF extremely accurately and in this region is superior to the predictions of the VACF obtained from the real lobe of the INM DOS. Although the model predicts a zero self-diffusivity, the predicted relaxation times are in better agreement with the molecular dynamics results when compared with those obtained from the INM theory. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious. [ABSTRACT FROM AUTHOR]

By using the formula first derived by Kirkwood, the friction coefficient of a solvated Brownian particle is determined from the integration on time of the autocorrelation function of the force that the solvent exerts on this particle. Extensive molecular dynamics simulations show that above a definite size of the studied systems the value of the integral defining the friction coefficient goes to a quasiconstant value (a plateau) when the upper bound on time increases. The minimal value of the system size where the integral exhibits this asymptotic behavior, rises with the Brownian particle size. From the plateau, a reliable estimate of the friction coefficient is obtained. [ABSTRACT FROM AUTHOR]

Highlights • A novel bootstrap method is proposed to infer auto-correlation structure of a random field. • The method proposed in this study is non-parametric and data driven, and is applicable to non-stationary random field data. • Both simulated and real data are used to demonstrate and validate the proposed method. Abstract Bootstrapping is a simulation-based method to provide statistical inference, or to measure accuracy, on estimators of interest, such as random field parameters, i.e., mean and variance of a physical quantity at each location within a random field, and auto-correlation coefficient of the quantities among any two different locations of the field. Statistical inference may be performed using analytical methods together with multiple sets of complete measurement data. However, measurement data may be incomplete sometimes, due to, for examples, sensor failure, storage capacity in collecting measurement data, or cost/difficulty in increasing measurement density. In such a case, it is challenging to perform statistical inference of auto-correlation coefficients, particularly between locations where no data are measured directly. This study aims to develop an innovative and robust bootstrap method to perform statistical inference on auto-correlation coefficient in one dimensional random fields, e.g., time series or variations of a quantity along a spatial direction such as depth, from multiple sets of incomplete and sparse measurements. The proposed approach is based on a Bayesian compressive sampling (BCS)-Karhunen–Loève (KL) expansion random field generator, which has been used to generate random field samples from sparse measurement data. The proposed bootstrap method is illustrated and validated through both numerical examples and real measurement data. The results obtained from the proposed bootstrap method using multiples sets of incomplete measurement data are consistent with those obtained from analytical methods using multiple sets of complete measurement data. [ABSTRACT FROM AUTHOR]

In this simulation study, regressions specified with autocorrelation effects are compared against those with relationship heterogeneity effects, and in doing so, provides guidance on their use. Regressions investigated are: (1) multiple linear regression, (2) a simultaneous autoregressive error model, and (3) geographically weighted regression. The first is nonspatial and acts as a control, the second accounts for stationary spatial autocorrelation via the error term, while the third captures spatial heterogeneity through the modeling of nonstationary relationships between the response and predictor variables. The geostatistical‐based simulation experiment generates data and coefficients with known multivariate spatial properties, all within an area‐unit spatial setting. Spatial autocorrelation and spatial heterogeneity effects are varied and accounted for. On fitting the regressions, that each have different assumptions and objectives, to very different geographical processes, valuable insights to their likely performance are uncovered. Results objectively confirm an inherent interrelationship between autocorrelation and heterogeneity, that results in an identification problem when choosing one regression over another. Given this, recommendations on the use and implementation of these spatial regressions are suggested, where knowledge of the properties of real study data and the analytical questions being posed are paramount. [ABSTRACT FROM AUTHOR]

In a large variety of applications, the data for a variable we wish to explain are ordered and categorical. In this paper, we present a new similarity-based model for the scenario and investigate its properties. We establish that the process is ψ-mixing and strictly stationary and derive the explicit form of the autocorrelation function in some special cases. Consistency and asymptotic normality of the maximum likelihood estimator of the model's parameters are proven. A simulation study supports our findings. The results are applied to the Netflix data set, comprised of a survey on users' grading of movies. [ABSTRACT FROM AUTHOR]

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7-8):2261-2278. 10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15-32. 10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability. [ABSTRACT FROM AUTHOR]

The popular diagnostic checking methods in linear time series models are portmanteau tests based on either residual autocorrelation functions (acf) or partial autocorrelation functions (pacf). In this paper, we device some new weighted mixed portmanteau tests by appropriately combining individual tests based on both acf and pacf. We derive the asymptotic distribution of such weighted mixed portmanteau statistics and study their size and power. It is found that the weighted mixed tests outperform when higher order ARMA models are fitted and diagnostic checks are performed via testing lack of residual autocorrelations. Simulation results suggest to use the proposed tests as complementary to those classical tests found in literature. An illustrative application is given to demonstrate the usefulness of the mixed test. [ABSTRACT FROM AUTHOR]

The authors discuss a special case of multiple time series models in which the autoregressive and moving average parameter matrices are diagonal. These models, called MTS-D, are appropriate if the random shocks which drive the series are only contemporaneously correlated. The flexibility and usefulness of this approach are illustrated in the context of a competitive market situation with five sales series, where individual components follow different univariate time series models. Theoretical as well as empirical justifications for expecting more precise parameter estimates are given. Forecasts from the model compare favorably with those from the univariate models. [ABSTRACT FROM AUTHOR]

As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the time scale of the latent space while inferring a reduced coordinate, which assists in finding slow processes as according to the variational approach to conformational dynamics. We provide evidence that the VDE framework [Hernández et al., Phys. Rev. E 97, 062412 (2018)], which uses this autocorrelation loss along with a time-lagged reconstruction loss, obtains a variationally optimized latent coordinate in comparison with related loss functions. We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes. [ABSTRACT FROM AUTHOR]

We give an introduction to the calculation of path integrals on a lattice, with the quantum harmonic oscillator as an example. In addition to providing an explicit computational setup and corresponding pseudocode, we pay particular attention to the existence of autocorrelations and the calculation of reliable errors. The over-relaxation technique is presented as a way to counter strong autocorrelations. The simulation methods can be extended to compute observables for path integrals in other settings. [ABSTRACT FROM AUTHOR]

Lead poisoning produces serious health problems, which are worse when a victim is younger. The US government and society have tried to prevent lead poisoning, especially since the 1970s; however, lead exposure remains prevalent. Lead poisoning analyses frequently use georeferenced blood lead level data. Like other types of data, these spatial data may contain uncertainties, such as location and attribute measurement errors, which can propagate to analysis results. For this paper, simulation experiments are employed to investigate how selected uncertainties impact regression analyses of blood lead level data in Syracuse, New York. In these simulations, location error and attribute measurement error, as well as a combination of these two errors, are embedded into the original data, and then these data are aggregated into census block group and census tract polygons. These aggregated data are analyzed with regression techniques, and comparisons are reported between the regression coefficients and their standard errors for the error added simulation results and the original results. To account for spatial autocorrelation, the eigenvector spatial filtering method and spatial autoregressive specifications are utilized with linear and generalized linear models. Our findings confirm that location error has more of an impact on the differences than does attribute measurement error, and show that the combined error leads to the greatest deviations. Location error simulation results show that smaller administrative units experience more of a location error impact, and, interestingly, coefficients and standard errors deviate more from their true values for a variable with a low level of spatial autocorrelation. These results imply that uncertainty, especially location error, has a considerable impact on the reliability of spatial analysis results for public health data, and that the level of spatial autocorrelation in a variable also has an impact on modeling results. [ABSTRACT FROM AUTHOR]

A simple ansatz for the study of velocity autocorrelation functions in fluids at different timescales is proposed. The ansatz is based on an effective summation of the infinite continued fraction at a reasonable assumption about convergence of relaxation times of the higher order memory functions, which have a purely kinetic origin. The VAFs obtained within our approach are compared with the results of the Markovian approximation for memory kernels. It is shown that although in the "overdamped" regime both approaches agree to a large extent at the initial and intermediate times of the system evolution, our formalism yields power law relaxation of the VAFs which is not observed at the description with a finite number of the collective modes. Explicit expressions for the transition times from kinetic to hydrodynamic regimes are obtained from the analysis of the singularities of spectral functions in the complex frequency plane. [ABSTRACT FROM AUTHOR]

We explore the estimation effectiveness of spatial lag models in the presence of missing observations. Spatial lag models are used to measure interdependency between dependent variables. If there are no missing data, it is easy to interpret this spatial autocorrelation process. Very sparsely sampled data are sometimes used in empirical studies. For such data, we observe only a small part of a population containing possible mutual dependencies. Simulation studies based on artificial data confirm the relation between the sampling rate and selection ratio of spatial and non-spatial models. Our findings include the following: (1) Negative spatial autocorrelation of the data-generating process (DGP) may not be observed. (2) Positive spatial autocorrelation of the DGP may be observed, but it is downward-biased. (3) We obtain less-biased estimates if we use a non-row-standardized weight matrix. (4) Non-spatial models tend to be selected in preference to the correct model, the spatial lag model. (5) Estimates of regression coefficients remain almost unbiased. [ABSTRACT FROM AUTHOR]

Brillouin spectroscopy is a powerful tool for measuring the mechanical properties of materials without contact. The sensitivity to mechanical changes that a Brillouin spectrometer can detect is determined by the precision to which a spectral peak can be localized. The localization precision is however fundamentally limited by the low number of photons within a Brillouin measurement, as well as by intrinsic noise of the setup. Here, we present a method to improve the spectral sensitivity of Brillouin measurements by exploiting the autocorrelation function of the spectrum. We show that by performing a localization process on the autocorrelation function nearly 20% increase in localization precision can be obtained. This result is consistent between our theoretical treatment, numerical simulation and experimental results. We further study the effect of background noise on the precision improvement for realistic scenarios. [ABSTRACT FROM AUTHOR]

In our paper we investigate the applicability of independent and identically distributed random sequences, first order Markov and higher order Markov chains as well as semi-Markov processes for bottom-up electricity load modeling. We use appliance time series from publicly available data sets containing fine grained power measurements. The comparison of models are based on metrics which are supposed to be important in power systems like Load Factor, Loss of Load Probability. Furthermore, we characterize the interdependence structure of the models with autocorrelation function as well. The aim of the investigation is to choose the most appropriate and the most parsimonious models for Smart Grid simulation purposes and applications like Demand Side Management and load scheduling. According to our results most appliance types can be modeled adequately with two states (on/off model) and the semi-Markov process can reproduce the properties of an aggregate load well compared to the original time series. With the price of more parameters of the semi-Markov model compared to identically distributed random sequence and first order Markov chain, it gives better results when the autocorrelation function, Loss of Load Probability and Load Factor are considered. [ABSTRACT FROM AUTHOR]

In conventional stochastic computation, all the input streams are Bernoulli sequences (BSs), which may result in large random error. To reduce random error and improve computational accuracy, some other sequences have been reported as alternatives to BSs. However, these sequences only apply to the specific stochastic circuits, have difficulties in hardware generation, or have length constraints. To this end, new sequences without these disadvantages should be considered. This paper proposes the random error analysis method for stochastic computation based on autocorrelation sequence (AS), which is more general than the conventional one based on BS. The analysis results show that we can use the proper ASs as input streams of stochastic circuits to reduce random error. On the basis of that conclusion, we propose the random error reduction scheme based on maximal concentrated autocorrelation sequence (MCAS) and BS, both of which are ASs. MCAS and BS are applicable to any combinational stochastic circuit, are easily generated by hardware, and have no length constraints, which avoid the disadvantages of sequences in the previous work. Moreover, we apply the proposed random error reduction scheme into several typical stochastic circuits as case studies. The simulation results confirm the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Using traditional control charts to monitor autocorrelated processes is not beneficial, because it will lead us to misleading detections in the processes. One of the methods used to deal with the control charts for autocorrelated process is the model-based approach. It uses an adequate time series model that fits the process and uses the residuals as monitoring statistics. For the said purpose, it is important to pick a suitable model that can adequately be used for different designs of control charts under specific time series model. This study intends to do the same for three popular types of charts namely Shewhart, exponentially weighted moving average, and cumulative sum. The models covered in this study include AR(1), MA(1), and ARMA(1,1) as the potential models to fit the process of interest. We have focused on two performance aspects namely efficiency and robustness. Average run length is used as a performance measure for different in-control and out-of-control states of the autocorrelated processes under varying levels of autocorrelation. An application example based on a real data set is also included in the study to highlight the importance of the study proposals. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions. [ABSTRACT FROM AUTHOR]

In this study, a set of geological simulations of the LabMag iron ore deposit in Labrador, Canada, is generated in order to quantify the geological risk in a pre-existing life of mine production schedule. The approach employs, firstly, min/max autocorrelation factors with missing data that allows for the joint geostatistical simulation of spatially correlated variables of interest for large-scale deposits such as LabMag. The seven structural layers of the deposit are simulated with the layer thicknesses acting as the set of correlated variables of interest. Secondly, four correlated geochemical properties within each domain (layer) are simulated. The simulated scenarios of LabMag represent the uncertainty of the deposit’s attributes of interest and are subsequently utilised to quantify and assess the risk of an existing mine plan, in terms of meeting production targets and generating the cash flows expected. The LabMag mine plan assessed by quantifying risk in its production forecasts is based on a single conventional (deterministic) deposit model. [ABSTRACT FROM AUTHOR]

Specific emitter identification use external signal feature called radio frequency fingerprint to identify the different radio transmitter, which does not rely on information content. A radio frequency fingerprint feature extraction approach based on I/Q imbalance of quadrature modulation signal is proposed. A I/Q modulator model is utilized to investigate the sources of radio frequency fingerprint and the effect on signal. A fingerprint features extraction based on signal space is proposed, but the SNR (Signal to Noise Ratio) need to be estimated before. Simulation results demonstrate that the method have superior performance than previous bispectrum-based, Hibert-Huang transform-based approaches. [ABSTRACT FROM AUTHOR]

The Single Variable Exchange algorithm is based on a simple idea; any model that can be simulated can be estimated by producing draws from the posterior distribution. We build on this simple idea by framing the Exchange algorithm as a mixture of Metropolis transition kernels and propose strategies that automatically select the more efficient transition kernels. In this manner we achieve significant improvements in convergence rate and autocorrelation of the Markov chain without relying on more than being able to simulate from the model. Our focus will be on statistical models in the Exponential Family and use two simple models from educational measurement to illustrate the contribution. [ABSTRACT FROM AUTHOR]

In this paper, we consider the multi-bit Differential Power Analysis ( DPA) in the Hamming weight model. In this regard, we revisit the definition of Transparency Order ( $$\mathsf {TO}$$ ) from the work of Prouff (FSE 2005) and find that the definition has certain limitations. Although this work has been quite well referred in the literature, surprisingly, these limitations remained unexplored for almost a decade. We analyse the definition from scratch, modify it and finally provide a definition with better insight that can theoretically capture DPA in Hamming weight model for hardware implementation with precharge logic. At the end, we confront the notion of (revised) transparency order with attack simulations in order to study to what extent the low transparency order of an s-box impacts the efficiency of a side channel attack against its processing. To the best of our knowledge, this is the first time that such a critical analysis is conducted (even considering the original notion of Prouff). It practically confirms that the transparency order is indeed related to the resistance of the s-box against side-channel attacks, but it also shows that it is not sufficient alone to directly achieve a satisfying level of security. Regarding this point, our conclusion is that the (revised) transparency order is a valuable criterion to consider when designing a cryptographic algorithm, and even if it does not preclude to also use classical countermeasures like masking or shuffling, it enables to improve their effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a new control scheme is proposed for Phase-II monitoring of simple linear profiles in multistage processes. In this scheme, an approach based on the U transformation is first applied to remove the effect of the cascade property involved in multistage processes. Then, a single max-EWMA-3 control statistic is derived based on the adjusted parameter estimates for simultaneous monitoring of all the parameters of a simple linear profile in each stage. Not only is the proposed scheme able to detect both increasing and decreasing shifts but it also has the feature of identifying the out-of-control parameter responsible for the source of process shift. Using extensive simulation experiments, the performance of the proposed method is evaluated and is compared with the ones of some other available methods for both weak and strong autocorrelations. Moreover, the diagnostic performance of the proposed method is evaluated. The results show that the proposed scheme performs well and works better than the competing methods. The application of the proposed method is illustrated at the end using a numerical example. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We propose to use AR-Sieve Bootstrap in the construction of a control chart of an autocorrelated process influenced by multiple exogenous inputs. The control charts are compared with Exponentially Weighted Moving Average (EWMA) control chart through a simulation study. AR-Sieve bootstrap control limits are narrower than EWMA control limits. While the proposed method yields a higher rate of false alarms, it is quick in detecting even minimal structural changes. [ABSTRACT FROM AUTHOR]

In this paper we examine in detail some of the modeling capabilities of the stationary m -state BMAP , with simultaneous events up to size k , noted BMAP m ( k ) . Specifically, we study the forms of the auto-correlation functions of the inter-event times and event sizes. We provide a novel characterization of the functions which is suitable for analyzing the dependence patterns. In particular, this allows one to prove a geometrically decrease to zero of the functions and to identify four correlation patterns, when m =2 . The case m ≥ 3 is illustrated via an extensive simulation study, from which it can be deduced that, as expected, richer structures can be obtained as m increases. In addition, the influence of the dependence patterns for both auto-correlation functions for the BMAP 2 ( 2 ) in the counting process has been explored through an empirical analysis. [ABSTRACT FROM AUTHOR]

Accurate mapping of catch rates of fish species is important for identifying their abundances and locations, and for monitoring their population changes. However, the use of imprecise fishing survey data may influence mapping and delineation of locations with high catch rates. We evaluated the incorporation of measurement uncertainty in recreational catch rate data for three important recreational fish species with different behavioural and distributional characteristics. The study combined a soft indicator kriging approach that uses a logistic function transformation and p -field simulation to determine the effect of measurement uncertainty on catch rate mapping and fishing area delineation. The results suggest that measurement uncertainty can influence the degree of spatial autocorrelation and the precision of kriging estimation. In addition, measurement uncertainty was found to influence the probability of classifying a location as either being a high or low catch rate zone. In general, the incorporation of the measurement uncertainty improves the ability to draw valid conclusions about the estimation results, which may influence any decision regarding the delineation of areas of abundance for spatial management. The use of a soft indicator kriging approach is a promising tool for catch rate mapping, in particular for survey data with class-specific patterns of spatial continuity. [ABSTRACT FROM AUTHOR]

By undertaking a large scale simulation study, we demonstrate that the maximum entropy bootstrap (meboot) data generation process can provide accurate and narrow parameter confidence intervals in models with combinations of stationary and nonstationary variables, under both low and high degrees of autocorrelation. The relatively small sample sizes in which meboot performs particularly well make it a useful tool for rolling window estimation. As a case study, we analyze the evolution of the price and income elasticities of import demand for crude oil in Turkey by using quarterly data between 1996-2011. Our approach can be employed to tackle a wide range of macroeconometric estimation problems where small sample sizes are a common issue. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new class of robust autocorrelation estimators based on interpreting the sample autocorrelation function as a linear regression. We investigate the efficiency and robustness properties of the estimators that result from employing three common robust regression techniques. We discuss the construction of robust autocovariance and positive definite autocorrelation estimates, and their application to AR model fitting. We perform simulation studies with various outlier configurations to compare the different estimators. [ABSTRACT FROM AUTHOR]

The accurate determination of control limits is crucial in statistical process control. The usual approach consists in computing the limits so that the in-control run-length distribution has some desired properties; for example, a prescribed mean. However, as a consequence of the increasing complexity of process data, the run-length of many control charts discussed in the recent literature can be studied only through simulation. Furthermore, in some scenarios, such as profile and autocorrelated data monitoring, the limits cannot be tabulated in advance, and when different charts are combined, the control limits depend on a multidimensional vector of parameters. In this article, we propose the use of stochastic approximation methods for control chart calibration and discuss enhancements for their implementation (e.g., the initialization of the algorithm, an adaptive choice of the gain, a suitable stopping rule for the iterative process, and the advantages of using multicore workstations). Examples are used to show that simulated stochastic approximation provides a reliable and fully automatic approach for computing the control limits in complex applications. An R package implementing the algorithm is available in the supplemental materials. [ABSTRACT FROM PUBLISHER]

In this paper, we propose some tests in the time domain to check the equality of autocorrelation structure of multiple () independent stationary time series. To develop the tests, multiple time series are divided into two groups in different ways. Given thatktime series are in one group andtime series are in the other group, test statistics,are the maximum Mahalanobis difference between 2 groups of time series over all possible ways of such grouping. The asymptotic distributions under the null are derived. An extensive simulation is conducted to check the finite sample properties of the test statistics. Suggestion is given on the selection of test statistics under different situations based on the simulation. The paper provides some options to compare multiple time series when the length of time series is relatively short andMis relatively large. An application of the tests on vibrational data analysis is discussed. [ABSTRACT FROM AUTHOR]

In this article, a transformation method using the principal component analysis approach is first applied to remove the existing autocorrelation within each profile in Phase I monitoring of autocorrelated simple linear profiles. This easy-to-use approach is independent of the autocorrelation coefficient. Moreover, since it is a model-free method, it can be used for Phase I monitoring procedures. Then, five control schemes are proposed to monitor the parameters of the profile with uncorrelated error terms. The performances of the proposed control charts are evaluated and are compared through simulation experiments based on different values of autocorrelation coefficient as well as different shift scenarios in the parameters of the profile in terms of probability of receiving an out-of-control signal. [ABSTRACT FROM PUBLISHER]