Discrimination of Individual Tigers (\u3cem\u3ePanthera tigris\u3c/em\u3e) from Long Distance Roars

This paper investigates the extent of tiger (Panthera tigris) vocal individuality through both qualitative and quantitative approaches using long distance roars from six individual tigers at Omaha\u27s Henry Doorly Zoo in Omaha, NE. The framework for comparison across individuals includes statistical and discriminant function analysis across whole vocalization measures and statistical pattern classification using a hidden Markov model (HMM) with frame-based spectral features comprised of Greenwood frequency cepstral coefficients. Individual discrimination accuracy is evaluated as a function of spectral model complexity, represented by the number of mixtures in the underlying Gaussian mixture model (GMM), and temporal model complexity, represented by the number of sequential states in the HMM. Results indicate that the temporal pattern of the vocalization is the most significant factor in accurate discrimination. Overall baseline discrimination accuracy for this data set is about 70% using high level features without complex spectral or temporal models. Accuracy increases to about 80% when more complex spectral models (multiple mixture GMMs) are incorporated, and increases to a final accuracy of 90% when more detailed temporal models (10-state HMMs) are used. Classification accuracy is stable across a relatively wide range of configurations in terms of spectral and temporal model resolution

Results of the ROTOR-program. I. The long-term photometric variability of classical T Tauri stars

We present a unique, homogeneous database of photometric measurements for Classical T Tauri stars extending up to 20 years. The database contains more than 21,000 UBVR observations of 72 CTTs. All the data were collected within the framework of the ROTOR-program at Mount Maidanak Observatory (Uzbekistan) and together they constitute the longest homogeneous, accurate record of TTS variability ever assembled. We characterize the long term photometric variations of 49 CTTs with sufficient data to allow a robust statistical analysis and propose an empirical classification scheme. Several patterns of long term photometric variability are identified. The most common pattern, exhibited by a group of 15 stars which includes T Tau itself, consists of low level variability (Delta(V)<=0.4mag) with no significant changes occurring from season to season over many years. A related subgroup of 22 stars exhibits a similar stable long term variability pattern, though with larger amplitudes (up to Delta(V)~1.6 mag). Besides these representative groups, we identify three smaller groups of 3-5 stars each which have distinctive photometric properties. The long term variability of most CTTs is fairly stable and merely reflects shorter term variability due to cold and hot surface spots. Only a small fraction of CTTs undergo significant brightness changes on the long term (months, years), which probably arise from slowly varying circumstellar extinction.Comment: 16 pages, 11 figures. Astron. Astrophys., in pres

Exploring disparities and similarities in European food consumption patterns

This paper investigates the heterogeneity of food consumption patterns in Europe. The analysis relies on a wide set of indicators, namely the structure of calorie, protein and fat consumption as well as the consumption of main foodstuffs. Clusters based on estimated income elasticity of calorie and protein demand are also reported. Income elasticities of animal products tend to exceed those corresponding to the total calorie demand. The same pattern holds true for the elasticity of demand for proteins. Main dimensions of consumption are identified based on factor analysis and used subsequently for the purpose of clustering countries. The hard core clusters are those that remain stable regardless of the algorithm used in classification or the indicators as a proxy of food consumption patterns. A limited number of hard core clusters of countries emerged. The paper concludes with a discussion of clusters with homogeneous patterns of consumption.food consumption patterns, Europe, factor analysis, cluster analysis, hard-core clusters

Pattern classification of valence in depression

Copyright @ The authors, 2013. This is an open access article available under Creative Commons Licence, CC-BY-NC-ND 3.0.Neuroimaging biomarkers of depression have potential to aid diagnosis, identify individuals at risk and predict treatment response or course of illness. Nevertheless none have been identified so far, potentially because no single brain parameter captures the complexity of the pathophysiology of depression. Multi-voxel pattern analysis (MVPA) may overcome this issue as it can identify patterns of voxels that are spatially distributed across the brain. Here we present the results of an MVPA to investigate the neuronal patterns underlying passive viewing of positive, negative and neutral pictures in depressed patients. A linear support vector machine (SVM) was trained to discriminate different valence conditions based on the functional magnetic resonance imaging (fMRI) data of nine unipolar depressed patients. A similar dataset obtained in nine healthy individuals was included to conduct a group classification analysis via linear discriminant analysis (LDA). Accuracy scores of 86% or higher were obtained for each valence contrast via patterns that included limbic areas such as the amygdala and frontal areas such as the ventrolateral prefrontal cortex. The LDA identified two areas (the dorsomedial prefrontal cortex and caudate nucleus) that allowed group classification with 72.2% accuracy. Our preliminary findings suggest that MVPA can identify stable valence patterns, with more sensitivity than univariate analysis, in depressed participants and that it may be possible to discriminate between healthy and depressed individuals based on differences in the brain's response to emotional cues.This work was supported by a PhD studentship to I.H. from the National Institute for Social Care and Health Research (NISCHR) HS/10/25 and MRC grant G 1100629

A VLSI-design of the minimum entropy neuron

One of the most interesting domains of feedforward networks is the processing of sensor signals. There do exist some networks which extract most of the information by implementing the maximum entropy principle for Gaussian sources. This is done by transforming input patterns to the base of eigenvectors of the input autocorrelation matrix with the biggest eigenvalues. The basic building block of these networks is the linear neuron, learning with the Oja learning rule. Nevertheless, some researchers in pattern recognition theory claim that for pattern recognition and classification clustering transformations are needed which reduce the intra-class entropy. This leads to stable, reliable features and is implemented for Gaussian sources by a linear transformation using the eigenvectors with the smallest eigenvalues. In another paper (Brause 1992) it is shown that the basic building block for such a transformation can be implemented by a linear neuron using an Anti-Hebb rule and restricted weights. This paper shows the analog VLSI design for such a building block, using standard modules of multiplication and addition. The most tedious problem in this VLSI-application is the design of an analog vector normalization circuitry. It can be shown that the standard approaches of weight summation will not give the convergence to the eigenvectors for a proper feature transformation. To avoid this problem, our design differs significantly from the standard approaches by computing the real Euclidean norm. Keywords: minimum entropy, principal component analysis, VLSI, neural networks, surface approximation, cluster transformation, weight normalization circuit

An Analytical Survey on Vein Pattern Recognition

Biometric is term of science to identify a person identity using their physiological features. Currently, vein pattern recognition has attracted the attention of the technology and industry all over the world. A vein is network of blood vessels under the skin of an individual. The vascular pattern is different for every person in the same part or region of the body. It is stable till very long age. As the veins are underneath the skin it is very difficult for intruder or forger to copy the feature. This uniqueness and strong immunity from intruders make it more potent biometric system which avails us secure features for individual identity verification. This paper involves the description of vein pattern recognition, its requirement and its importance in biometric system. Different feature extraction algorithms are reviewed as independent component analysis, principal component analysis method. For classification in vein pattern recognition we have reviewed support vector machine and neural network techniques. Parameters are described based on which results are computed like true positive, false positive, true negative, false negative, accuracy and precision

Voxel selection in fMRI data analysis based on sparse representation

Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method

International specialization models in Latin America: the case of Argentina

The paper compares the Argentine specialization model with that of the other major Latin American countries. Given the lack of production data at disaggregate level, we rely on trade flow information from the WTA Statistics Canada database (3-digit SITC classification), available for most Latin American countries for a rather long time span (1980-2000). Our analysis, based on the Lafay Index of international specialization, shows that Argentina concentrates its comparative advantages in raw materials, agricultural and food products and exhibits, at the same time, serious deficiencies in the production of manufactures. This specialization pattern has remained remarkably stable over the last two decades, in spite of the major reforms implemented in many different fields. These features are shared with the other major Latin American countries, with the notable exception of Mexico, whose comparative advantages have changed dramatically in the same period, from raw materials (essentially oil) towards manufactures. Moreover, the products in which Argentina is specialized are among those for which world demand growth is structurally lower; this could eventually lead to a decreasing weight of Argentina in international markets.International trade; specialization model; revealed comparative advantages

Iris Image Recognition Based on Independent Component Analysis and Support Vector Machine

Iris has a very unique texture and pattern, different for each individual and the pattern will remain stable, making it possible as biometric technology called iris recognition. In this paper, 150 iris image from Dept. Computer Science, Palacky University in Olomouc iris database used for iris recognition based on independent component analysis and support vector machine. There are three steps for developing this research namely, image preprocessing, feature extraction and recognition. First step is image preprocessing in order to get the iris region from eye image. Second is feature extraction by using independent component analysis in order to get the feature from iris image. Support vector machine (SVM) is used for iris classification and recognition. In the end of this experimental, the implement method will evaluated based upon Genuine Acceptance Rate (GAR). Experimental result shown that the recognize rate from variation of training data is 52% with one data train, 73% with two data train and 90% three data train. From experimental result also shows that this technique produces good performance.