Hierarchical clustering based web service discovery

This paper presents a hierarchical clustering method for semantic Web service discovery. This method aims to improve the accuracy and efficiency of the traditional service discovery using vector space model. The Web service is converted into a standard vector format through the Web service description document. With the help of WordNet, a semantic analysis is conducted to reduce the dimension of the term vector and to make semantic expansion to meet the user’s service request. The process and algorithm of hierarchical clustering based semantic Web service discovery is discussed. Validation is carried out on the dataset.

Dimensionality reduction assisted tensor clustering

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

Serial clustering of extratropical cyclones in a multi-model ensemble of historical and future simulations

This study has investigated serial (temporal) clustering of extra-tropical cyclones simulated by 17 climate models that participated in CMIP5. Clustering was estimated by calculating the dispersion (ratio of variance to mean) of 30 December-February counts of Atlantic storm tracks passing nearby each grid point. Results from single historical simulations of 1975-2005 were compared to those from historical ERA40 reanalyses from 1958-2001 ERA40 and single future model projections of 2069-2099 under the RCP4.5 climate change scenario. Models were generally able to capture the broad features in reanalyses reported previously: underdispersion/regularity (i.e. variance less than mean) in the western core of the Atlantic storm track surrounded by overdispersion/clustering (i.e. variance greater than mean) to the north and south and over western Europe. Regression of counts onto North Atlantic Oscillation (NAO) indices revealed that much of the overdispersion in the historical reanalyses and model simulations can be accounted for by NAO variability. Future changes in dispersion were generally found to be small and not consistent across models. The overdispersion statistic, for any 30 year sample, is prone to large amounts of sampling uncertainty that obscures the climate change signal. For example, the projected increase in dispersion for storm counts near London in the CNRMCM5 model is 0.1 compared to a standard deviation of 0.25. Projected changes in the mean and variance of NAO are insufficient to create changes in overdispersion that are discernible above natural sampling variations.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

Evaluation of a polynya flux model by means of thermal infrared satellite estimates

We test the ability of a two-dimensional flux model to simulate polynya events with narrow open-water zones by comparing model results to ice-thickness and ice-production estimates derived from thermal infrared Moderate Resolution Imaging Spectroradiometer (MODIS) observations in conjunction with an atmospheric dataset. Given a polynya boundary and an atmospheric dataset, the model correctly reproduces the shape of an 11 day long event, using only a few simple conservation laws. Ice production is slightly overestimated by the model, owing to an underestimated ice thickness. We achieved best model results with the consolidation thickness parameterization developed by Biggs and others (2000). Observed regional discrepancies between model and satellite estimates might be a consequence of the missing representation of the dynamic of the thin-ice thickening (e.g. rafting). We conclude that this simplified polynya model is a valuable tool for studying polynya dynamics and estimating associated fluxes of single polynya events.

Deletion of Tumor Necrosis Factor-alpha Receptor 1 (TNFR1) Protects against Diet-induced Obesity by Means of Increased Thermogenesis

In diet-induced obesity, hypothalamic and systemic inflammatory factors trigger intracellular mechanisms that lead to resistance to the main adipostatic hormones, leptin and insulin. Tumor necrosis factor-alpha (TNF-alpha) is one of the main inflammatory factors produced during this process and its mechanistic role as an inducer of leptin and insulin resistance has been widely investigated. Most of TNF-alpha inflammatory signals are delivered by TNF receptor 1 (R1); however, the role played by this receptor in the context of obesity-associated inflammation is not completely known. Here, we show that TNFR1 knock-out (TNFR1 KO) mice are protected from diet-induced obesity due to increased thermogenesis. Under standard rodent chow or a high-fat diet, TNFR1 KO gain significantly less body mass despite increased caloric intake. Visceral adiposity and mean adipocyte diameter are reduced and blood concentrations of insulin and leptin are lower. Protection from hypothalamic leptin resistance is evidenced by increased leptin-induced suppression of food intake and preserved activation of leptin signal transduction through JAK2, STAT3, and FOXO1. Under the high-fat diet, TNFR1 KO mice present a significantly increased expression of the thermogenesis-related neurotransmitter, TRH. Further evidence of increased thermogenesis includes increased O(2) consumption in respirometry measurements, increased expressions of UCP1 and UCP3 in brown adipose tissue and skeletal muscle, respectively, and increased O(2) consumption by isolated skeletal muscle fiber mitochondria. This demonstrates that TNF-alpha signaling through TNFR1 is an important mechanism involved in obesity-associated defective thermogenesis.

The role of endothelin pathway in modulation of airway reactivity to methacholine in C57Bl/6 and BALB/c mice

Endothelin peptides have been shown to increase cholinergic neurotransmission in the airway. Genetic differences in airway responsiveness to methacholine where reported in mice. The present study compared the airway reactivity to methacholine in C57Bl/6 and BALB/c mice, the involvement of endothelin on this reactivity and endothelin levels in lung homogenates. Whole airway reactivity was analyzed by means of an isolated lung preparation where lungs were perfused through the trachea with warm gassed Krebs solution at 5 ml/min, and changes in perfusion pressure triggered by methacholine at increasing bolus doses (0.1-100 mu g) were recorded. We found that the maximal airway response to methacholine was much greater in C57Bl/6 than in BALB/c (Emax 34 +/- 2 vs 12 +/- 1 cmH(2)O, respectively). Bosentan (mixed endothelin A/B receptor antagonist; 10 mg/kg, i.p., 30 min before sacrifice) reduced lung responsiveness to methacholine in C57Bl/6 (58% at EC50 level) but had no effect in BALB/c mouse strain. This effect seems to be mediated by the endothelin ETA receptor since it was significantly reduced by the selective endothelin ETA receptor antagonist, BQ 123. Immunoreactive endothelin levels were higher in C57Bl/6 than in BALB/c lungs (43 5 vs 19 +/- 5 pg/g of tissue). In conclusion, airway reactivity to methacholine and lung endothelins content varies markedly between C57Bl/6 and BALB/c strains. Endothelins upregulate lung responsiveness to methacholine only in C57Bl/6, an effect achieved through the endothelin ETA receptor. (C) 2008 Elsevier B.V. All rights reserved.

Characterization of spliced leader genes of Trypanosoma (Megatrypanum) theileri: phylogeographical analysis of Brazilian isolates from cattle supports spatial clustering of genotypes and parity with ribosomal markers

Trypanosoma (Megatrypanum) theileri from cattle and trypanosomes of other artiodactyls form a clade of closely related species in analyses using ribosomal sequences. Analysis of polymorphic sequences of a larger number of trypanosomes from broader geographical origins is required to evaluate the Clustering of isolates as suggested by previous studies. Here, we determined the sequences of the spliced leader (SL) genes of 21 isolates from cattle and 2 from water buffalo from distant regions of Brazil. Analysis of SL gene repeats revealed that the 5S rRNA gene is inserted within the intergenic region. Phylogeographical patterns inferred using SL sequences showed at least 5 major genotypes of T. theileri distributed in 2 strongly divergent lineages. Lineage TthI comprises genotypes IA and IB from buffalo and cattle, respectively, from the Southeast and Central regions, whereas genotype IC is restricted to cattle from the Southern region. Lineage Tth II includes cattle genotypes IIA, which is restricted to the North and Northeast, and IIB, found in the Centre, West, North and Northeast. PCR-RFLP of SL genes revealed valuable markers for genotyping T. theileri. The results of this study emphasize the genetic complexity and corroborate the geographical structuring of T. theileri genotypes found in cattle.

Comparative phylogeography of Trypanosoma cruzi TCIIc: New hosts, association with terrestrial ecotopes, and spatial clustering

We characterized 28 new isolates of Trypanosoma cruzi IIc (TCIIc) of mammals and triatomines from Northern to Southern Brazil, confirming the widespread distribution of this lineage. Phylogenetic analyses using cytochrome b and SSU rDNA sequences clearly separated TCIIc from TCIIa according to terrestrial and arboreal ecotopes of their preferential mammalian hosts and vectors. TCIIc was more closely related to TCIId/e, followed by TCIIa, and separated by large distances from TCIIb and TCI. Despite being indistinguishable by traditional genotyping and generally being assigned to Z3, we provide evidence that TCIIa from South America and TCIIa from North America correspond to independent lineages that circulate in distinct hosts and ecological niches. Armadillos, terrestrial didelphids and rodents, and domestic dogs were found infected by TCIIc in Brazil. We believe that, in Brazil, this is the first description of TCIIc from rodents and domestic dogs. Terrestrial triatomines of genera Panstrongylus and Triatoma were confirmed as vectors of TCIIc. Together, habitat, mammalian host and vector association corroborated the link between TCIIc and terrestrial transmission cycles/ecological niches. Analysis of ITS1 rDNA sequences disclosed clusters of TCIIc isolates in accordance with their geographic origin, independent of their host species. (C) 2009 Elsevier B.V. All rights reserved.

Partitions selection strategy for set of clustering solutions

Clustering is a difficult task: there is no single cluster definition and the data can have more than one underlying structure. Pareto-based multi-objective genetic algorithms (e.g., MOCK Multi-Objective Clustering with automatic K-determination and MOCLE-Multi-Objective Clustering Ensemble) were proposed to tackle these problems. However, the output of such algorithms can often contains a high number of partitions, becoming difficult for an expert to manually analyze all of them. In order to deal with this problem, we present two selection strategies, which are based on the corrected Rand, to choose a subset of solutions. To test them, they are applied to the set of solutions produced by MOCK and MOCLE in the context of several datasets. The study was also extended to select a reduced set of partitions from the initial population of MOCLE. These analysis show that both versions of selection strategy proposed are very effective. They can significantly reduce the number of solutions and, at the same time, keep the quality and the diversity of the partitions in the original set of solutions. (C) 2010 Elsevier B.V. All rights reserved.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

Indirect study of (11)B(p,alpha(0))(8)Be and (10)B(p,alpha)(7)Be reactions at astrophysical energies by means of the Trojan Horse Method: recent results

Nuclear (p,alpha) reactions destroying the so-called ""light-elements"" lithium, beryllium and boron have been largely studied in the past mainly because their role in understanding some astrophysical phenomena, i.e. mixing-phenomena occurring in young F-G stars [1]. Such mechanisms transport the surface material down to the region close to the nuclear destruction zone, where typical temperatures of the order of similar to 10(6) K are reached. The corresponding Gamow energy E(0)=1.22 (Z(x)(2)Z(X)(2)T(6)(2))(1/3) [2] is about similar to 10 keV if one considers the ""boron-case"" and replaces in the previous formula Z(x) = 1, Z(X) = 5 and T(6) = 5. Direct measurements of the two (11)B(p,alpha(0))(8)Be and (10)B(p,alpha)(7)Be reactions in correspondence of this energy region are difficult to perform mainly because the combined effects of Coulomb barrier penetrability and electron screening [3]. The indirect method of the Trojan Horse (THM) [4-6] allows one to extract the two-body reaction cross section of interest for astrophysics without the extrapolation-procedures. Due to the THM formalism, the extracted indirect data have to be normalized to the available direct ones at higher energies thus implying that the method is a complementary tool in solving some still open questions for both nuclear and astrophysical issues [7-12].

Optical and dielectric relaxor behaviour of Ba(Zr(0.25)Ti(0.75))O(3) ceramic explained by means of distorted clusters

In this work, Ba(Zr(0.25)Ti(0.75))O(3) ceramic was prepared by solid-state reaction. This material was characterized by x-ray diffraction and Fourier transform Raman spectroscopy. The temperature dependent dielectric properties were investigated in the frequency range from 1 kHz to 1 MHz. The dielectric measurements indicated a diffuse phase transition. The broadening of the dielectric permittivity in the frequency range as well as its shifting at higher temperatures indicated a relaxor-like behaviour for this material. The diffusivity and the relaxation strength were estimated using the modified Curie-Weiss law. The optical properties were analysed by ultraviolet-visible (UV-vis) absorption spectroscopy and photoluminescence (PL) measurements at room temperature. The UV-vis spectrum indicated that the Ba(Zr(0.25)Ti(0.75))O(3) ceramic has an optical band gap of 2.98 eV. A blue PL emission was observed for this compound when excited with 350 nm wavelength. The polarity as well as the PL property of this material was attributed to the presence of polar [TiO(6)] distorted clusters into a globally cubic matrix.