A generalized data windowing scheme for adaptive conjugate gradient algorithms

The performance of the modified adaptive conjugate gradient (CG) algorithms based on the iterative CG method for adaptive filtering is highly related to the ways of estimating the correlation matrix and the cross-correlation vector. The existing approaches of implementing the CG algorithms using the data windows of exponential form or sliding form result in either loss of convergence or increase in misadjustment. This paper presents and analyzes a new approach to the implementation of the CG algorithms for adaptive filtering by using a generalized data windowing scheme. For the new modified CG algorithms, we show that the convergence speed is accelerated, the misadjustment and tracking capability comparable to those of the recursive least squares (RLS) algorithm are achieved. Computer simulations demonstrated in the framework of linear system modeling problem show the improvements of the new modifications.

CAFS : a novel lightweight cache-based scheme for large-scale intrusion alert fusion

In this paper, we present some practical experiences on implementing an alert fusion mechanism from our project. After investigation on most of the existing alert fusion systems, we found the current body of work alternatively weighed down in the mire of insecure design or rarely deployed because of their complexity. As confirmed by our experimental analysis, unsuitable mechanisms could easily be submerged by an abundance of useless alerts. Even with the use of methods that achieve a high fusion rate and low false positives, attack is also possible. To find the solution, we carried out analysis on a series of alerts generated by well-known datasets as well as realistic alerts from the Australian Honey-Pot. One important finding is that one alert has more than an 85% chance of being fused in the following five alerts. Of particular importance is our design of a novel lightweight Cache-based Alert Fusion Scheme, called CAFS. CAFS has the capacity to not only reduce the quantity of useless alerts generated by intrusion detection system, but also enhance the accuracy of alerts, therefore greatly reducing the cost of fusion processing. We also present reasonable and practical specifications for the target-oriented fusion policy that provides a quality guarantee on alert fusion, and as a result seamlessly satisfies the process of successive correlation. Our experiments compared CAFS with traditional centralized fusion. The results showed that the CAFS easily attained the desired level of simple, counter-escapable alert fusion design. Furthermore, as a lightweight scheme, CAFS can easily be deployed and excel in a large amount of alert fusions, which go towards improving the usability of system resources. To the best of our knowledge, our work is a practical exploration in addressing problems from the academic point of view. Copyright © 2011 John Wiley & Sons, Ltd.

Optimising errors in signaling corporate collapse using MCCCRA

Purpose – The purpose of this paper is to put forward an innovative approach for reducing the variation between Type I and Type II errors in the context of ratio-based modeling of corporate collapse, without compromising the accuracy of the predictive model. Its contribution to the literature lies in resolving the problematic trade-off between predictive accuracy and variations between the two types of errors.

Design/methodology/approach – The methodological approach in this paper – called MCCCRA – utilizes a novel multi-classification matrix based on a combination of correlation and regression analysis, with the former being subject to optimisation criteria. In order to ascertain its accuracy in signaling collapse, MCCCRA is empirically tested against multiple discriminant analysis (MDA).

Findings – Based on a data sample of 899 US publicly listed companies, the empirical results indicate that in addition to a high level of accuracy in signaling collapse, MCCCRA generates lower variability between Type I and Type II errors when compared to MDA.

Originality/value – Although correlation and regression analysis are long-standing statistical tools, the optimisation constraints that are applied to the correlations are unique. Moreover, the multi-classification matrix is a first in signaling collapse. By providing economic insight into more stable financial modeling, these innovations make an original contribution to the literature.

Application of rank correlation, clustering and classification in information security

This article is devoted to experimental investigation of a novel application of a clustering technique introduced by the authors recently in order to use robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on a particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, rank correlation is used to select a subset of features for dimensionality reduction. We investigate the effectiveness of the Pearson Linear Correlation Coefficient, the Spearman Rank Correlation Coefficient and the Goodman--Kruskal Correlation Coefficient in this application. Third, we use a consensus function to combine independent initial clusterings into one consensus clustering. Fourth, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for the effectiveness of the whole procedure. We investigated various combinations of several correlation coefficients, consensus functions, and a variety of supervised classification algorithms.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

The asymptotic distribution of the CADF unit root test in the presence of heterogeneous AR(p) errors

The CADF test of Pesaran (J Appl Econ 22:265–312, 2007) are among the most popular univariate tests for cross-section correlated panels around. Yet, the existing asymptotic analysis of this test statistic is limited to a model in which the errors are assumed to follow a simple AR(1) structure with homogenous autoregressive coefficients. One reason for this is that the model involves an intricate identification issue, as both the serial and cross-section correlation structures of the errors are unobserved. The purpose of the current paper is to tackle this issue and in so doing extend the existing analysis to the case of AR((Formula presented.)) errors with possibly heterogeneous coefficients.

Testing slope homogeneity in large panels with serial correlation

Pesaran and Yamagata (Pesaran, M.H., Yamagata, T., Testing slope homogeneity in large panels, Journal of Econometrics 142, 50-93, 2008) propose a test for slope homogeneity in large panels, which has become very popular in the literature. However, the test cannot deal with the practically relevant case of heteroskedastic and/serially correlated errors. The present note proposes a generalized test that accommodates both features. © 2013 Elsevier B.V.

Correlation between the fighting rates of REM sleep-deprived rats and susceptibility to the 'wild running' of audiogenic seizures

Sleep-deprived rats exhibit defensive fighting as well as explosive flights very similar to the wild-running of audiogenic seizures. In order to determine why sleep deprivation is a common factor that facilitates both panic and convulsive manifestations, the present study was undertaken to investigate whether rats that display sleep deprivation-induced fighting (SDIF) are the same as those that are susceptible to audiogenic wild-running (WR). Twenty-eight male adult Wistar rats were divided into two groups assigned to two e-sleep deprivation for 5 days and had their SDIF evaluated in social experimental schemes. In the first, 18 subjects were submitted to REM grouping. After 1 week for recovery, their susceptibility to WR was tested in an acoustic stimulation trial ( 104 dB, 200 Hz, 60 S). Rats that did not present WR received a lactate infusion and were tested again by acoustic stimulation 40 min later. In the second experimental scheme, 10 subjects were initially evaluated for WR susceptibility and the number of SDIF was recorded in social grouping after I week. Three categories of WR-susceptibility were determined: WR-sensitive rats, intermediate WR-sensitive rats and WR-insensitive rats. T'he number of SDIF in each category was significantly different and there was a high positive correlation (r=0.89; Spearman test) between the number of SDIF and the level of WR-susceptibility. We conclude that the reasons why sleep deprivation exerts facilitatory effects on both panic and convulsive manifestations are due to overlappings of neural pathways responsible for both behavioral patterns and for the property of sleep deprivation to increase neuronal excitability. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Sources of errors in the quantitative analysis of food carotenoids by HPLC

Several factors render carotenoid determination inherently difficult. Thus, in spite of advances in analytical instrumentation, discrepancies in quantitative results on carotenoids can be encountered in the international literature. A good part of the errors comes from the pre-chromatographic steps such as sampling scheme that does not yield samples representative of the food lots under investigation; sample preparation which does not maintain representativity and guarantee homogeneity of the analytical sample; incomplete extraction; physical losses of carotenoids during the various steps, especially during partition or washing and by adsorption to glass walls of containers; isomerization and oxidation of carotenoids during analysis. on the otherhand, although currently considered the method of choice for carotenoids, high performance liquid chromatography (HPLC) is subject to various sources of errors, such as: incompatibility of the injection solvent and the mobile phase, resulting in distorted or split peaks; erroneous identification; unavailability, impurity and instability of carotenoid standards; quantification of highly overlapping peaks; low recovery from the HPLC column; errors in the preparation of standard solutions and in the calibration procedure; calculation errors. Illustrations of the possible errors in the quantification of carotenoids by HPLC are presented.

Mesonic correlation functions at finite temperature in the NJL model

We employ the NJL model to calculate mesonic correlation functions at finite temperature and compare results with recent lattice QCD simulations. We employ an implicit regularization scheme to deal with the divergent amplitudes to obtain ambiguity-free, scale-invariant and symmetry-preserving physical amplitudes. Making the coupling constants of the model temperature dependent, we show that at low momenta our results agree qualitatively with lattice simulations.

Hadronic current correlation functions at finite temperature in the NJL model

Recently there have been suggestions that for a proper description of hadronic matter and hadronic correlation functions within the NJL model at finite density/temperature the parameters of the model should be taken density/temperature dependent. Here we show that qualitatively similar results can be obtained using a cutoff-independent regularization of the NJL model. In this regularization scheme one can express the divergent parts at finite density/temperature of the amplitudes in terms of their counterparts in vacuum.

Cosmological forecasts from photometric measurements of the angular correlation function

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Ab Initio correlated all electron dirac-fock calculations for Eka-Francium Fluoride (E119F)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ab Initio correlated all electron Dirac-Fock calculations for eka-francium fluoride (E119F)

Resultados obtidos por cálculos Dirac-Fock correlacionados de 4 componentes para o fluoreto do elemento E119 (Eka-Frâncio) com base estável e precisa, livre de prolapso variacional, são reportados neste trabalho. No nível CCSD(T), a distância de equilíbrio R_e, frequência harmônica ω_e e energia de dissociação D_e são 2,432 Å, 354,97 cm_-1 e 116,92 kcal mol_-1, respectivamente. Também são reportados base livre de prolapso variacional de 4 componentes para o elemento 119, uma curva analítica de energia potencial precisa e o espectro vibracional a partir dos dados obtidos no nível CCSD(T). Nossos resultados sugerem que a molécula E119F deva ser menos iônica que seus fluoretos alcalinos homólogos mais leves, em contraste com o senso químico comum baseado nas propriedades periódicas - era de se esperar nesta molécula a ligação química mais iônica possível. Também encontramos que a correção do tipo modelo de carga para negligenciar as integrais do tipo SS resulta em erros insignificantes e acelera os cálculos cerca de 3 vezes no nível CCSD(T) e cerca de 4 vezes no nível DFT/B3LYP.

Protecting the root SWAP operation from general and residual errors by continuous dynamical decoupling

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)