Low Autocorrelation Research Articles

Spatial analysis spotlighting early childhood leprosy transmission in a hyperendemic municipality of the Brazilian Amazon region.

BackgroundMore than 200,000 new cases of leprosy were reported by 105 countries in 2011. The disease is a public health problem in Brazil, particularly within high-burden pockets in the Amazon region where leprosy is hyperendemic among children.MethodologyWe applied geographic information systems and spatial analysis to determine the spatio-temporal pattern of leprosy cases in a hyperendemic municipality of the Brazilian Amazon region (Castanhal). Moreover, we performed active surveillance to collect clinical, epidemiological and serological data of the household contacts of people affected by leprosy and school children in the general population. The occurrence of subclinical infection and overt disease among the evaluated individuals was correlated with the spatio-temporal pattern of leprosy.Principal FindingsThe pattern of leprosy cases showed significant spatio-temporal heterogeneity (p<0.01). Considering 499 mapped cases, we found spatial clusters of high and low detection rates and spatial autocorrelation of individual cases at fine spatio-temporal scales. The relative risk of contracting leprosy in one specific cluster with a high detection rate is almost four times the risk in the areas of low detection rate (RR = 3.86; 95% CI = 2.26–6.59; p<0.0001). Eight new cases were detected among 302 evaluated household contacts: two living in areas of clusters of high detection rate and six in hyperendemic census tracts. Of 188 examined students, 134 (71.3%) lived in hyperendemic areas, 120 (63.8%) were dwelling less than 100 meters of at least one reported leprosy case, 125 (66.5%) showed immunological evidence (positive anti-PGL-I IgM titer) of subclinical infection, and 9 (4.8%) were diagnosed with leprosy (8 within 200 meters of a case living in the same area).Conclusions/SignificanceSpatial analysis provided a better understanding of the high rate of early childhood leprosy transmission in this region. These findings can be applied to guide leprosy control programs to target intervention to high risk areas.

Effect of Price Changes on the Supply Response of Rubber in Nigeria (1980-2010)

The study on the effect of price changes on rubber supply in Nigeria was carried out through the application of the Nerlovian partial adjustment model. Time series data covering a period of 30 years were obtained from the National Bureau of Statistics, Central Bank of Nigeria (CBN) Annual Agricultural Survey and the Central Bank of Nigeria Statistical Bulletin. The Nerlovian model was specified on the basis of the functional relationship between rubber supply and the lagged endogenous variables of rubber supply as well as the export price of rubber for the period under review. The findings revealed that previous year's output used as a lagged endogenous variable had a positive magnitude of 0.768 and was significant at 1%. The positive sign and the proof of significance is consistent with economic theory which posits that output has a direct relationship with the previous production level. The magnitude of the export price co-efficient of 0.0012 was low and not significant. Though the sign is in line with theoretical expectation, the low value may be attributable to the spurious nature of the estimates of time series due to trending effect. The coefficient of determination (R 2 ) of 76.4% indicates the proportion of the variation in natural rubber supply that was explained by the specified model. Also the Durbin Watson statistic value of 2.24 showed the presence of low autocorrelation. It is therefore recommended that an appropriate mechanism for price stabilization be instituted by governments as means of enhancing high export trade volume, since price is found to be a major determinant of rubber export trade in Nigeria. Keywards: Price changes, Supply Response, Nigeria

A Genetic Algorithm for Generating Radar Transmit Codes to Minimize the Target Profile Estimation Error

AbstractThis article presents the design and development of a genetic algorithm (GA) to generate long-range transmit codes with low autocorrelation side lobes for radar to minimize target profile estimation error. The GA described in this work has a parallel processing design and has been used to generate codes with multiple constellations for various code lengths with low estimated error of a radar target profile.

Factors of Hurst Exponent

Under the Fractal Theory research, a stock with high Hurst Exponent shall have high autocorrelation for the share price and we should use trend following investment method to profit from the stock trend. On the other hand, if a stock is with low Hurst Exponent, this means that the stock price shall have low autocorrelation and we should use the range trading investment method to profit from the without trend up and down. This research checks which factors are important factors affecting the Hurst Exponent of the share price of a company so that we know which investment method should be used: trend following or range trading.

Linear complexity of quaternary sequences of length [formula omitted] with low autocorrelation

We derive the linear complexity of quaternary sequences of length pq with low autocorrelation over the finite field of four elements and over the finite ring of order 4. Also we examine the linear complexity of the other sequences of length pq.

Unifying measures of biodiversity: understanding when richness and phylogenetic diversity should be congruent

AbstractAimBiogeographical theory and conservation valuation schemes necessarily involve assessing how biodiversity is distributed through space and ‘biodiversity’ encapsulates many different aspects of biological organization and information. While biogeography may try to explain biodiversity patterns, successful conservation strategies should attempt to maximize different aspects of diversity. Ultimately, diversity patterns are the product of evolutionary history, and research and conservation efforts seek to understand the unequal distribution of evolutionary history. For conservation efforts, results have been inconsistent as to whether species richness (SR) provides sufficient surrogacy for evolutionary history. Here, we provide a conceptual framework allowing for the direct comparison of taxonomic richness and phylogenetic diversity (PD), both in terms of their mechanistic relationship and the relationship between their spatial distributions.LocationGlobal.MethodsWe present a framework that relates regional SR, PD, biogeographically weighted evolutionary distinctiveness and biogeographically weighted SR. Further, we use simulations to illustrate how the size of the species pool, topological patterns within the phylogeny and autocorrelation in spatial distributions affect the correlation among metrics.ResultsIn regions that include both recently diversified groups and ancient species poor lineages, large species pools and low spatial autocorrelation, the correlation between biodiversity measures is lower than regions with low richness, balanced phylogenetic trees and high spatial autocorrelation.Main conclusionsWe can now understand and predict when regional richness and PD should be strongly correlated. This congruency is the product of evolutionary and ecological processes that determine species pool membership and community assembly. Further, in regions where SR is not expected to be congruent with phylogenetic distinctiveness, re‐examining how existing reserve networks protect the multiple aspects of biodiversity is critically important.

Detecting Sedimentary Cycles using Autocorrelation of Grain size

Detection of sedimentary cycles is difficult in fine-grained or homogenous sediments but is a prerequisite for the interpretation of depositional environments. Here we use a new autocorrelation analysis to detect cycles in a homogenous sediment core, E602, from the northern shelf of the South China Sea. Autocorrelation coefficients were calculated for different mean grain sizes at various depths. The results show that sediments derived from rapid depositional events have a better autocorrelation. Analysis of two other cores confirms this result. Cores composed of sediments deposited quickly under stable and/or gradually changing hydrodynamic conditions, have higher autocorrelation coefficients, whereas, those composed of sediments deposited during calm periods have relatively low autocorrelation coefficients. It shows that abrupt changes in autocorrelation coefficients usually indicate the existence of a boundary between adjacent sedimentary cycles, with each cycle beginning with a high positive autocorrelation coefficient of grain size and ending with a low negative one.

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

Current multivariate control charts for monitoring large scale industrial processes are typically based on latent variable models, such as principal component analysis (PCA) or its dynamic counterpart when variables present auto-correlation (DPCA). In fact, it is usually considered that, under such conditions, DPCA is capable to effectively deal with both the cross- and auto-correlated nature of data. However, it can easily be verified that the resulting monitoring statistics (T2 and Q, also referred by SPE) still present significant auto-correlation. To handle this issue, a set of multivariate statistics based on DPCA and on the generation of decorrelated residuals were developed, that present low auto-correlation levels, and therefore are better positioned to implement SPC in a more consistent and stable way (DPCA-DR). The monitoring performance of these statistics was compared with that from other alternative methodologies for the well-known Tennessee Eastman process benchmark. From this study, we conclude that the proposed statistics had the highest detection rates on 19 out of the 21 faults, and are statistically superior to their PCA and DPCA counterparts. DPCA-DR statistics also presented lower auto-correlation, which simplifies their implementation and improves their reliability.

A Mixed Integer Quadratic Programming Model for the Low Autocorrelation Binary Sequence Problem

In this paper the low autocorrelation binary sequence problem (LABSP) is modeled as a mixed integer quadratic programming (MIQP) problem and proof of the model’s validity is given. Since the MIQP model is semidefinite, general optimization solvers can be used, and converge in a finite number of iterations. The experimental results show that IQP solvers, based on this MIQP formulation, are capable of optimally solving general/skew-symmetric LABSP instances of up to 30/51 elements in a moderate time. ACM Computing Classification System (1998): G.1.6, I.2.8.

Polyphase orthogonal sequences design for opportunistic array radar via HGA

Opportunistic array radar (OAR) is a new generation radar system based on the stealth of the platform, which can improve the modern radar performance effectively. Designing the orthogonal code sets with low autocorrelation and cross-correlation is a key issue for OAR. This paper proposes a novel hybrid genetic algorithm (HGA) and designs the polyphase orthogonal code sets with low autocorrelation and cross-correlation properties, which can be used in the OAR system. The novel algorithm combines with simulated annealing (SA) and genetic algorithm (GA), adds in keeping best individuals and competition in small scope, and introduces grey correlation evaluation to evaluate fitness function. These avoid the premature convergence problem existed in GA and enhance the global searching capability. At last, the genetic results are optimized to obtain the best solution by using greedy algorithm. The simulation results show that the proposed algorithm is effective for the design of orthogonal phase signals used in OAR systems.

On the Linear Complexity of a Class of Quaternary Sequences with Low Autocorrelation

On the Linear Complexity of a Class of Quaternary Sequences with Low Autocorrelation

Characteristics of Video Traffic from Videoconference Applications: From H.261 to H.264

This paper presents modelling results for H.26x video traffic generated by popular videoconference software applications. The analysis of videoconference data, that were measured during realistic point-point videoconference sessions, led us to the general conclusion that the traffic can be distinguished into two categories: unconstrained and constrained. In the unconstrained traffic, there is a direct relation between the encoder and the form of the frequency histogram of the frame-size sequence. Moreover, for this type of traffic, strong correlations between successive video frames can be found. On the other hand, where bandwidth constraints are imposed during the encoding process, the generated traffic appears to exhibit similar characteristics for all the examined encoders with the very low autocorrelation values being the most notable one. On the basis of these results, this study proposes methods to calculate the parameters of a widely adopted autoregressive model for both types of traffic.

Nonparametric HAC Estimation for Time Series Data with Missing Observations

The Newey and West (1987) estimator has become the standard way to estimate a heteroskedasticity and autocorrelation consistent (HAC) covariance matrix, but it does not immediately apply to time series with missing observations. We demonstrate that the intuitive approach to estimate the true spectrum of the underlying process using only the observed data leads to incorrect inference. Instead, we propose two simple consistent HAC estimators for time series with missing data. First, we develop the Amplitude Modulated estimator by applying the Newey-West estimator and treating the missing observations as non-serially correlated. Secondly, we develop the Equal Spacing estimator by applying the Newey-West estimator to the series formed by treating the data as equally spaced. We show asymptotic consistency of both estimators for inference purposes and discuss finite sample variance and bias tradeoff. In Monte Carlo simulations, we demonstrate that the Equal Spacing estimator is preferred in most cases due to its lower bias, while the Amplitude Modulated estimator is preferred for small sample size and low autocorrelation due to its lower variance.

Nonparametric HAC Estimation for Time Series Data with Missing Observations

The Newey and West (1987) estimator has become the standard way to estimate a heteroskedasticity and autocorrelation consistent (HAC) covariance matrix, but it does not immediately apply to time series with missing observations. We demonstrate that the intuitive approach to estimate the true spectrum of the underlying process using only the observed data leads to incorrect inference. Instead, we propose two simple consistent HAC estimators for time series with missing data. First, we develop the Amplitude Modulated estimator by applying the Newey-West estimator and treating the missing observations as non-serially correlated. Secondly, we develop the Equal Spacing estimator by applying the Newey-West estimator to the series formed by treating the data as equally spaced. We show asymptotic consistency of both estimators for inference purposes and discuss finite sample variance and bias tradeoff. In Monte Carlo simulations, we demonstrate that the Equal Spacing estimator is preferred in most cases due to its lower bias, while the Amplitude Modulated estimator is preferred for small sample size and low autocorrelation due to its lower variance.

On the Distribution of Velocity Measurements From Pulse-to-Pulse Coherent Doppler Sonar

Knowledge of the velocity measurement distribution is useful for designing new coherent Doppler systems, for resolving velocity ambiguity, and for optimally combining measurements from multiple receivers and multiple carrier frequencies. In this paper, the effects of pulse-pair averaging and pulse-to-pulse autocorrelation are analyzed for ensembles consisting of up to ten pulse pairs. A formula is presented for the probability distribution of velocity measurements from a single pulse pair. This distribution is nonnormal for all values of the autocorrelation coefficient. In the limit of perfect pulse-to-pulse correlation, single pulse-pair measurements obey a Pearson Type VII distribution which has a higher peak and broader tails than a normal distribution. Second and fourth moments of the multiple pulse-pair measurement distributions are evaluated using a Monte Carlo method. The applicability of perturbation analysis for predicting velocity standard deviation is investigated. In the analysis of Zrnić (1977), it is assumed that perturbations in autocorrelation phase are small compared to the mean phase. Three prediction failure mechanisms are demonstrated through changes in the distribution of the complex autocorrelation coefficient; these occur for both high and low autocorrelation magnitude, and for short ensemble lengths. Measurement distributions from the Monte Carlo method are also used to determine the autocorrelation magnitude necessary to resolve velocity ambiguity using a dual-frequency coherent Doppler sonar. Simulations with a high-fidelity coherent Doppler sonar model show how the distribution of velocity measurements is affected by finite particle concentrations. While velocity standard deviation is insensitive to concentration above a threshold of approximately 1 particle per sample volume, kurtosis exhibits a strong dependence on concentration in the range of 0.1-5 particles per sample volume.

ADDRESSING UTILITY SPACE COMPLEXITY IN NEGOTIATIONS INVOLVING HIGHLY UNCORRELATED, CONSTRAINT‐BASED UTILITY SPACES

There is an increasing interest in complex automated negotiations, where agents negotiate about multiple, interdependent issues and agent utility functions exhibit low autocorrelation. In these scenarios, the negotiation mechanisms used to find agreement solutions among agents tend to fail due to the complexity of agents’ preference spaces, and this tendency increases as the degree of autocorrelation decreases. In this paper, we propose an automated negotiation model specially tailored for highly uncorrelated utility spaces based on weighted constraints. The model relies on a mediated, auction‐based interaction protocol and a set of heuristic mechanisms for bidding and deal identification. To address the challenges raised by highly uncorrelated utility spaces, we propose to use a quality factor, which allows agents to balance utility and deal probability when placing their bids or when searching for agreement regions among these bids. Experiments show that the proposed negotiation model achieves high optimality results and low failure rates even in negotiation scenarios involving highly uncorrelated utility spaces, thus outperforming previous approaches.

Optimizing polyphase sequences for orthogonal netted radar systems

Orthogonal netted radar systems (ONRS) can fundamentally improve the radar performance by using a group of specially designed orthogonal polyphase code signals which require a very low aperiodic autocorrelation peak sidelobe level, low aperiodic cross-correlation, and a good resilience to small Doppler shifts. However, the existing numerical solutions degrade severely in the presence of small Doppler shifts. A new set of polyphase sequences is presented with good correlation properties as well as resilience to Doppler shifts. These sequences are built by using numerical optimization based on correlation properties as well as the Doppler effects on matched filter outputs, which maintains the Doppler tolerance. The statistical simulated annealing algorithm and the greedy code search method are used to optimize the sequences. Correlation and Doppler results are compared with the best-known sequences and show to be superior.

Large-scale structure in absorption: gas within and around galaxy voids

We investigate the properties of the HI Ly-a absorption systems (Ly-a forest) within and around galaxy voids at z<0.1. We find a significant excess (>99% c.l.) of Ly-a systems at the edges of galaxy voids with respect to a random distribution, on ~5 h^{-1} Mpc scales. We find no significant difference in the number of systems inside voids with respect to the random expectation. We report differences between both column density (N_{HI}) and Doppler parameter (b_{HI}) distributions of Ly-a systems found inside and at the edge of galaxy voids at the >98% and >90% c.l. respectively. Low density environments (voids) have smaller values for both N_{HI} and b_{HI} than higher density ones (edges of voids). These trends are theoretically expected and also found in GIMIC, a state-of-the-art hydrodynamical simulation. Our findings are consistent with a scenario of at least three types of Ly-alpha: (1) containing embedded galaxies and so directly correlated with galaxies (referred as `halo-like'), (2) correlated with galaxies only because they lie in the same over-dense LSS, and (3) associated with under-dense LSS with a very low auto-correlation amplitude (~ random) that are not correlated with luminous galaxies. We argue the latter arise in structures still growing linearly from the primordial density fluctuations inside galaxy voids that have not formed galaxies because of their low densities. We estimate that these under-dense LSS absorbers account for 25-30% +- 6% of the current Ly-a population (N_{HI} > 10^{12.5} cm^{-2}) while the other two types account for the remaining 70-75% +- 12%. Assuming that only N_{HI} > 10^{14} cm^{-2} systems have embedded galaxies nearby, we have estimated the contribution of the `halo-like' Ly-a to be ~12-15% +- 4% and consequently ~55-60% +- 13% of the Ly-a systems to be associated with the over-dense LSS.

Does stock return predictability affect ESO fair value?

Executive Stock Options (ESOs) are modified American options that cannot be valued using standard methods. With a few exceptions, the literature has discussed the ESO fair value by assuming unpredictable stock returns which are not supported by the available empirical evidence. In this paper we obtain the fair value of American ESOs when stock returns are predictable and, specifically, driven by the trending Ornstein–Uhlenbeck process of Lo and Wang (1995). We solve the executive’s portfolio allocation problem for a simple buy-and-hold strategy when his wealth can be distributed between a risk-free asset and a market portfolio. This problem is jointly solved with the executive’s optimal exercise policy. We find that executives tend to wait longer the higher the predictability, independently of the composition of executive’s asset menu. We have also analyzed the implications under the FAS123R proposals for the ESO fair value and found that, even for low autocorrelations, there is a meaningful mispricing when unpredictable returns are erroneously assumed.

An Automaton Approach for Waiting Times in DNA Evolution

In a recent article, Behrens and Vingron (J. Comput. Biol. 17/12, 2010) compute waiting times for k-mers to appear during DNA evolution under the assumption that the considered k-mers do not occur in the initial DNA sequence, an issue arising when studying the evolution of regulatory DNA sequences with regard to transcription factor (TF) binding site emergence. The mathematical analysis underlying their computation assumes that occurrences of words under interest do not overlap. We relax here this assumption by use of an automata approach. In an alphabet of size 4 like the DNA alphabet, most words have no or a low autocorrelation; therefore, globally, our results confirm those of Behrens and Vingron. The outcome is quite different when considering highly autocorrelated k-mers; in this case, the autocorrelation pushes down the probability of occurrence of these k-mers at generation 1 and, consequently, increases the waiting time for apparition of these k-mers up to 40%. An analysis of existing TF binding sites unveils a significant proportion of k-mers exhibiting autocorrelation. Thus, our computations based on automata greatly improve the accuracy of predicting waiting times for the emergence of TF binding sites to appear during DNA evolution. We do the computation in the Bernoulli or M0 model; computations in the M1 model, a Markov model of order 1, are more costly in terms of time and memory but should produce similar results. While Behrens and Vingron considered specifically promoters of length 1000, we extend the results to promoters of any size; we exhibit the property that the probability that a k-mer occurs at generation time 1 while being absent at time 0 behaves linearly with respect to the length of the promoter, which induces a hyperbolic behaviour of the waiting time of any k-mer with respect to the length of the promoter. The C code is available at www.lipn.univ-paris13.fr/∼nicodeme/ .