Cross Layer QoS Guarantees in Multiuser WLAN Systems

The use of real-time delay-sensitive applications in wireless systems has significantly increased during the last years. Consequently, the demand to guarantee certain Quality of Service (QoS) is a challenging issue for the system’s designers. A cross-layer based dynamically tuned queue length scheduler is presented in this paper, for the Downlink of multiuser WLAN systems with heterogeneous traffic requirements. An opportunistic scheduling algorithm is applied, while users from higher priority traffic classes are prioritized. A trade-off between the throughput maximization of the system and the guarantee of the users QoS requirements is obtained. Therefore the length of the queue is dynamically adjusted to select the appropriate conditions based on the operator requirements.     

A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized Dirichlet mixture.

This paper applies a robust statistical scheme to the problem of unsupervised learning of high-dimensional data. We develop, analyze, and apply a new finite mixture model based on a generalization of the Dirichlet distribution. The generalized Dirichlet distribution has a more general covariance structure than the Dirichlet distribution and offers high flexibility and ease of use for the approximation of both symmetric and asymmetric distributions. We show that the mathematical properties of this distribution allow high-dimensional modeling without requiring dimensionality reduction and, thus, without a loss of information. This makes the generalized Dirichlet distribution more practical and useful. We propose a hybrid stochastic expectation maximization algorithm (HSEM) to estimate the parameters of the generalized Dirichlet mixture. The algorithm is called stochastic because it contains a step in which the data elements are assigned randomly to components in order to avoid convergence to a saddle point. The adjective "hybrid" is justified by the introduction of a Newton-Raphson step. Moreover, the HSEM algorithm autonomously selects the number of components by the introduction of an agglomerative term. The performance of our method is tested by the classification of several pattern-recognition data sets. The generalized Dirichlet mixture is also applied to the problems of image restoration, image object recognition and texture image database summarization for efficient retrieval. For the texture image summarization problem, results are reported for the Vistex texture image database from the MIT Media Lab.     

Nonparametric variational learning of multivariate beta mixture models in medical applications

Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering method “variational learning of infinite multivariate Beta mixture models.” The motivation behind proposing this technique is the flexibility of mixture models to fit the data. This approach has the capability to infer the model complexity and estimate model parameters from the observed data automatically. Moreover, as a label‐free method, it could also address the problem of high costs of medical data labeling, which can be undertaken just by medical experts. The performance of the model is evaluated on real medical applications and compared with other similar alternatives. We demonstrate the ability of our proposed method to outperform widely used methods in the field as it has been shown in experimental results.     

On multivariate binary data clustering and feature weighting

This paper presents an approach that partitions data sets of unlabeled binary vectors without a priori information about the number of clusters or the saliency of the features. The unsupervised binary feature selection problem is approached using finite mixture models of multivariate Bernoulli distributions. Using stochastic complexity, the proposed model determines simultaneously the number of clusters in a given data set composed of binary vectors and the saliency of the features used. We conduct different applications involving real data, document classification and images categorization to show the merits of the proposed approach.     

Infinite Liouville mixture models with application to text and texture categorization

This paper addresses the problem of proportional data modeling and clustering using mixture models, a problem of great interest and of importance for many practical pattern recognition, image processing, data mining and computer vision applications. Finite mixture models are broadly applicable to clustering problems. But, they involve the challenging problem of the selection of the number of clusters which requires a certain trade-off. The number of clusters must be sufficient to provide the discriminating capability between clusters required for a given application. Indeed, if too many clusters are employed overfitting problems may occur and if few are used we have a problem of underfitting. Here we approach the problem of modeling and clustering proportional data using infinite mixtures which have been shown to be an efficient alternative to finite mixtures by overcoming the concern regarding the selection of the optimal number of mixture components. In particular, we propose and discuss the consideration of infinite Liouville mixture model whose parameter values are fitted to the data through a principled Bayesian algorithm that we have developed and which allows uncertainty in the number of mixture components. Our experimental evaluation involves two challenging applications namely text classification and texture discrimination, and suggests that the proposed approach can be an excellent choice for proportional data modeling.     

A countably infinite mixture model for clustering and feature selection

Mixture modeling is one of the most useful tools in machine learning and data mining applications. An important challenge when applying finite mixture models is the selection of the number of clusters which best describes the data. Recent developments have shown that this problem can be handled by the application of non-parametric Bayesian techniques to mixture modeling. Another important crucial preprocessing step to mixture learning is the selection of the most relevant features. The main approach in this paper, to tackle these problems, consists on storing the knowledge in a generalized Dirichlet mixture model by applying non-parametric Bayesian estimation and inference techniques. Specifically, we extend finite generalized Dirichlet mixture models to the infinite case in which the number of components and relevant features do not need to be known a priori. This extension provides a natural representation of uncertainty regarding the challenging problem of model selection. We propose a Markov Chain Monte Carlo algorithm to learn the resulted infinite mixture. Through applications involving text and image categorization, we show that infinite mixture models offer a more powerful and robust performance than classic finite mixtures for both clustering and feature selection.     

Improved Arabic-to-English statistical machine translation by reordering post-verbal subjects for word alignment

We study challenges raised by the order of Arabic verbs and their subjects in statistical machine translation (SMT). We show that the boundaries of post-verbal subjects (VS) are hard to detect accurately, even with a state-of-the-art Arabic dependency parser. In addition, VS constructions have highly ambiguous reordering patterns when translated to English, and these patterns are very different for matrix (main clause) VS and non-matrix (subordinate clause) VS. Based on this analysis, we propose a novel method for leveraging VS information in SMT: we reorder VS constructions into pre-verbal (SV) order for word alignment. Unlike previous approaches to source-side reordering, phrase extraction and decoding are performed using the original Arabic word order. This strategy significantly improves BLEU and TER scores, even on a strong large-scale baseline. Limiting reordering to matrix VS yields further improvements.     

Quantitative duplex real-time polymerase chain reaction assay with TaqMan probe detects and quantifies Crocodylus porosus in food chain and traditional medicines

Consumption and exploitation of crocodiles have been rampant for their exotic, nutritive and medicinal attributes. These depredations are alarming and although they have continued to be monitored by wildlife and conservation agencies, unlawful trading of crocodiles shows an increasing trend worldwide. Recently, conventional polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (RFLP) assays for crocodile have been documented but they are only suitable for identification and cannot quantify adulterations. We described here a quantitative duplex real-time PCR assay with probes to quantify contributions from Crocodylus porosus materials simultaneously. A very short amplicon size of 127bp was used because longer targets could have been broken down in samples, bringing considerable uncertainty in molecular analysis. We have validated a TaqMan probe-based duplex real-time PCR (qPCR) assay for the detection of 0.004 ng DNA in pure state and 0.1% target meat in model chicken meatball. False negative detection was eliminated through an endogenous control (141-bp site of eukaryotic 18S rRNA). Analysis of 12 model chicken meatballs adulterated with C. porosus reflected 96.3?120.2% target recovery at 0.1?10% adulterations. A validation test of 21 commercial food and traditional medicine (TM) crocodile-based products showed 100% effectiveness. Short amplicon sizes, alternative complementary target, exceptional stability and superior sensitivity suggested the assay could be used for the identification and quantitative determination of C. porosus in any food or TM samples even under degraded conditions.     

Insight into the electroreduction of nitrate ions at a copper electrode, in neutral solution, after determination of their diffusion coefficient by electrochemical impedance spectroscopy

The electrochemical reduction of nitrate ions at a copper electrode in an unbuffered neutral aqueous solution is studied. Using a two compartment electrochemical cell, three stationary cathodic waves, noted P1, P2 and P3, were evidenced by cyclic voltammetry at −0.9, −1.2 and −1.3 V/SCE, respectively. By comparing the electrochemical response of nitrate and nitrite containing solutions, P1 was attributed to the reduction of nitrate to nitrite. In order to assign P2 and P3 features by determining the number of electrons involved at the corresponding potential, rotating disk electrode experiments at various rotation speeds, combined with linear sweep voltammetry, were performed. Current data analysis at a given potential was carried out using Koutecky-Levich treatment taking into account water reduction. Confident values of the diffusion coefficient D of nitrate ions were assessed by electrochemical impedance spectroscopy for nitrate concentrations of 10⁻³, 10⁻² and 10⁻¹ M. For a nitrate concentration of 10⁻² M, D was found to be 1.31 × 10⁻⁵ cm² s⁻¹ allowing the number of electrons to be determined as 6 for P2 and 8 for P3, in accordance with nitrate reduction into hydroxylamine and ammonia, respectively. The formation of hydroxylamine was confirmed by the observation of its reoxidation at a Pt microelectrode present at the Cu electrode/nitrate solution interface.     

An Approach to the Synthesis of the Manzamine Alkaloids Via the Vinylogous Amide PHotocycloAddition/Retro-Mannich Fragmentation/Mannich Closure Cascade (pharM)

The application of the vinylogous amide [2+2] photocycloaddition/retro-Mannich fragmentation/ M annich closure cascade ( pharM ) to the synthesis of the pentacyclic ring system of the anti-leukemic marine alkaloid manzamine A is presented. Two approaches to the synthesis of the requisite pentacycle are described: (a) the transannular photocycloaddition of an 18-membered vinylogous amide; and (b) photocycloaddition of an acyclic vinylogous amide, followed by macrolactamization of the derived pharM closure product to generate the pentacyclic ring system.