Comment on "Performance of different synchronization measures in real data: A case study on electroencephalographic signals"

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Assessment of the RE(OH)3 Ising magnetic materials as possible candidates for the study of transverse-field-induced quantum phase transitions

The LiHoxY1−xF4 Ising magnetic material subject to a magnetic field perpendicular to the Ho3+ Ising direction has shown over the past 20 years to be a host of very interesting thermodynamic and magnetic phenomena. Unfortunately, the availability of other magnetic materials other than LiHoxY1−xF4 that may be described by a transverse-field Ising model remains very much limited. It is in this context that we use here a mean-field theory to investigate the suitability of the Ho(OH)3, Dy(OH)3, and Tb(OH)3 insulating hexagonal dipolar Ising-type ferromagnets for the study of the quantum phase transition induced by a magnetic field, Bx, applied perpendicular to the Ising spin direction. Experimentally, the zero-field critical (Curie) temperatures are known to be Tc≈2.54, 3.48, and 3.72 K, for Ho(OH)3, Dy(OH)3, and Tb(OH)3, respectively. From our calculations we estimate the critical transverse field, Bxc, to destroy ferromagnetic order at zero temperature to be Bxc=4.35, 5.03, and 54.81 T for Ho(OH)3, Dy(OH)3, and Tb(OH)3, respectively. We find that Ho(OH)3, similarly to LiHoF4, can be quantitatively described by an effective S=1/2 transverse-field Ising model. This is not the case for Dy(OH)3 due to the strong admixing between the ground doublet and first excited doublet induced by the dipolar interactions. Furthermore, we find that the paramagnetic (PM) to ferromagnetic (FM) transition in Dy(OH)3 becomes first order for strong Bx and low temperatures. Hence, the PM to FM zero-temperature transition in Dy(OH)3 may be first order and not quantum critical. We investigate the effect of competing antiferromagnetic nearest-neighbor exchange and applied magnetic field, Bz, along the Ising spin direction ẑ on the first-order transition in Dy(OH)3. We conclude from these preliminary calculations that Ho(OH)3 and Dy(OH)3 and their Y3+ diamagnetically diluted variants, HoxY1−x(OH)3 and DyxY1−x(OH)3, are potentially interesting systems to study transverse-field-induced quantum fluctuations effects in hard axis (Ising-type) magnetic materials.

Helium ion microscopy visualizes lipid nanodomains in mammalian cells

Cell membranes are composed of two-dimensional bilayers of amphipathic lipids, which allow a lateral movement of the respective membrane components. These components are arranged in an inhomogeneous manner as transient micro- and nanodomains, which are believed to be crucially involved in the regulation of signal transduction pathways in mammalian cells. Because of their small size (diameter 10-200 nm), membrane nanodomains cannot be directly imaged using conventional light microscopy. Here, we present direct visualization of cell membrane nanodomains by helium ion microscopy (HIM). We show that HIM is capable to image biological specimens without any conductive coating, and that HIM images clearly allow the identification of nanodomains in the ultrastructure of membranes with 1.5 nm resolution. The shape of these nanodomains is preserved by fixation of the surrounding unsaturated fatty acids while saturated fatty acids inside the nanodomains are selectively removed. Atomic force microscopy, fluorescence microscopy, 3D structured illumination microscopy and direct stochastic optical reconstruction microscopy provide additional evidence that the structures in the HIM images of cell membranes originate from membrane nanodomains. The nanodomains observed by HIM have an average diameter of 20 nm and are densely arranged with a minimal nearest neighbor distance of ~15 nm.

Crystal growth and magnetic properties of tin selenide-doped europium Sn(1-x)Eu(x)Se

The growth and magnetic properties of Tin Selenide (SnSe) doped with Eu(2+) Sn(1-x)Eu(x)Se (x=2.5%) were investigated. Q-band (34 GHz) electron paramagnetic resonance measurements show that the site symmetry of Eu(2+) at 4.2 K is orthorhombic and the Lande factor was determined to be g=1.99 +/- 0.01. The exchange coupling between nearest-neighbor (NN) Eu(2+) ions was estimated from magnetization and magnetic-susceptibility measurements using a model that takes into account the magnetic contributions of single ions, pairs and triplets. The exchange interaction between Eu(2+) nearest neighbors was found to be antiferromagnetic with an estimated average value of J(p)/k(B) =-0.18 +/- 0.03 K. (C) 2009 Elsevier B.V. All rights reserved.

X-ray and gamma irradiation effects on dipole defects in CaF2 : La, Yb, Al

Impurity-interstitial dipoles in calcium fluoride solutions with Al3+, Yb3+ and La3+ fluorides were studied using the thermally stimulated depolarization current (TSDC) technique. The dipolar complexes are formed by substitutional trivalent ions in Ca2+ sites and interstitial fluorine in nearest neighbor sites. The relaxations observed at 150 K are assigned to dipoles nnR(S)(3+)- F-i(-) (R-S = La or Yb). The purpose of this work is to study the processes of energy storage in the fluorides following X-ray and gamma irradiation. Computer modelling techniques are used to obtain the formation energy of dipole defects. (C) 2007 Elsevier Ltd. All rights reserved.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Texture recognition by using GLCM and various aggregation functions

We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods.We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification.

Using Choquet integrals for kNN approximation and classification

k-nearest neighbors (kNN) is a popular method for function approximation and classification. One drawback of this method is that the nearest neighbors can be all located on one side of the point in question x. An alternative natural neighbors method is expensive for more than three variables. In this paper we propose the use of the discrete Choquet integral for combining the values of the nearest neighbors so that redundant information is canceled out. We design a fuzzy measure based on location of the nearest neighbors, which favors neighbors located all around x.

Localized relaxational dynamics of succinonitrile

Succinonitrile (N≡C—CH₂—CH₂—C≡N) is a good ionic conductor, when doped with an ionic compound, at room temperature, where it is in its plastic crystalline phase (Long et al. Solid State Ionics 2003, 161, 105; Alarco et al. Nat. Mater. 2004, 3, 476). We report on the relaxational dynamics of the plastic phase near the two first-order phase transitions and on the effect of dissolving a salt in the plastic matrix by quasi-elastic neutron scattering. At 240 K, the three observed relaxations are localized and we can describe their dynamics (τ ≈ 1.7, 17, and 140 ps) to a certain extent from a model using a single molecule that was proposed by B_ée et al. allowing for all conformations in its unit cell (space group IM3M). The extent of the localized motion as observed is however larger than that predicted by the model and suggests that the isomerization of succinonitrile is correlated with a jump to the nearest neighbor site in the unit cell. The salt containing system is known to be a good ionic conductor, and our results show that the effect of the ions on the succinonitrile matrix is homogeneous. Because the isomerizations and rotations are governed by intermolecular interactions, the dissolved ions have an effect over an extended range. Due to the addition of the salt, the dynamics of one of the components (τ ≈ 17 ps) shows more diffusive character at 300 K. The calculated upper limit of the corresponding diffusion constant of succinonitrile in the electrolyte is a factor 30 higher than what is reported for the ions. Our results suggest that the succinonitrile diffusion is caused by nearest neighbor jumps that are localized on the observed length and time scales.

Zero-day malware detection based on supervised learning algorithms of API call signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k−Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO – PolyKernel, SMO – Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.

Fusion of GRNN and FA for online noisy data regression

A new online neural-network-based regression model for noisy data is proposed in this paper. It is a hybrid system combining the Fuzzy ART (FA) and General Regression Neural Network (GRNN) models. Both the FA and GRNN models are fast incremental learning systems. The proposed hybrid model, denoted as GRNNFA-online, retains the online learning properties of both models. The kernel centers of the GRNN are obtained by compressing the training samples using the FA model. The width of each kernel is then estimated by the K-nearest-neighbors (kNN) method. A heuristic is proposed to tune the value of Kof the kNN dynamically based on the concept of gradient-descent. The performance of the GRNNFA-online model was evaluated using two benchmark datasets, i.e., OZONE and Friedman#1. The experimental results demonstrated the convergence of the prediction errors. Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.

Use of circle-segments as a data visualization technique for feature selection in pattern classification

One of the issues associated with pattern classification using data based machine learning systems is the “curse of dimensionality”. In this paper, the circle-segments method is proposed as a feature selection method to identify important input features before the entire data set is provided for learning with machine learning systems. Specifically, four machine learning systems are deployed for classification, viz. Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy ARTMAP (FAM), and k-Nearest Neighbour (kNN). The integration between the circle-segments method and the machine learning systems has been applied to two case studies comprising one benchmark and one real data sets. Overall, the results after feature selection using the circle segments method demonstrate improvements in performance even with more than 50% of the input features eliminated from the original data sets.

EEG data classification using wavelet features selected by Wilcoxon statistics

This paper introduces a method to classify EEG signals using features extracted by an integration of wavelet transform and the nonparametric Wilcoxon test. Orthogonal Haar wavelet coefficients are ranked based on the Wilcoxon test’s statistics. The most prominent discriminant wavelets are assembled to form a feature set that serves as inputs to the naïve Bayes classifier. Two benchmark datasets, named Ia and Ib, downloaded from the brain–computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed combination of Haar wavelet features and naïve Bayes classifier considerably dominates the competitive classification approaches and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II. Application of naïve Bayes also provides a low computational cost approach that promotes the implementation of a potential real-time BCI system.

A structure-activity relationship study of quinone compounds with trypanocidal activity

A set of 25 quinone compounds with anti-trypanocidal activity was studied by using the density functional theory (DFT) method in order to calculate atomic and molecular properties to be correlated with the biological activity. The chemometric methods principal component analysis (PCA), hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA), Kth nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA) were used to obtain possible relationships between the calculated descriptors and the biological activity studied and to predict the anti-trypanocidal activity of new quinone compounds from a prediction set. Four descriptors were responsible for the separation between the active and inactive compounds: T-5 (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors give information on the kind of interaction that occurs between the compounds and the biological receptor. The prediction study was done with a set of three new compounds by using the PCA, HCA, SDA, KNN and SIMCA methods and two of them were predicted as active against the Trypanosoma cruzi. (c) 2005 Elsevier SAS. All rights reserved.

An objective reconstruction of the chain of contagion

North's clustering method, which is based on a much used ecological model, the nearest neighbor distance, was applied to the objective reconstruction of the chain of household-to-household transmission of variola minor (the mild form of smallpox). The discrete within-household outbreaks were considered as points which were ordered in a time sequence using a 10-40 day interval between introduction of the disease into a source household and a receptor household. The closer points in the plane were assumed to have a larger probability of being links of a chain of household-to-household spread of the disease. The five defining distances (Manhattan or city-block distance between presumptive source and receptor dwellings) were 100, 200, 300, 400 and 500 m. The subchain sets obtained with the five defining distances were compared with the subchains empirically reconstructed during the field study of the epidemic through direct investigation of personal contacts of the introductory cases with either introductory or subsequent cases from previously affected households. The criteria of fit of theoretical to empirical clusters were: (a) the number of clustered dwellings and subchains, (b) number of dwellings in a subchain and (c) position of dwellings in a subchain. The defining distance closet to the empirical findings was 200 m, which fully agrees with the travelling habits of the study population. Less close but acceptable approximations were obtained with 100, 300, 400 and 500 m. The latter two distances gave identical results, as if a clustering ceiling had been reached. It seems that North's clustering model may be used for an objective reconstruction of the chain of contagious whose links are discrete within-household outbreaks. © 1984.