Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference

Dificuldades da escolha do CSP para chamadas de longa distância

Tendo como ambiente as privatizações das empresas estatais ocorridas no final da década de 90 e a nova realidade de mercado, quando o que se vende são marcas e não produtos, a monografia terá como objetivo analisar o histórico da ampliação do número de marcas para um determinado serviço paralelo ao surgimento de uma dificuldade de escolha do mesmo para ser o consumido. No caso, será estudado o mercado de telecomunicações de longa distância no Brasil, levantando as diferenças e/ou semelhanças dos principais players. Teremos uma introdução completa do histórico das telecomunicações no país e do comportamento das marcas ao longo dos anos. Entenderemos como se dá essa dificuldade de escolha do código de seleção da prestadora (CSP) e como o consumidor está inserido nesse grande desafio

Succession of lignocellulolytic bacterial consortia bred anaerobically from lake sediment

Anaerobic bacteria degrade lignocellulose in various anoxic and organically rich environments, often in a syntrophic process. Anaerobic enrichments of bacterial communities on a recalcitrant lignocellulose source were studied combining polymerase chain reaction–denaturing gradient gel electrophoresis, amplicon sequencing of the 16S rRNA gene and culturing. Three consortia were constructed using the microbiota of lake sediment as the starting inoculum and untreated switchgrass (Panicum virgatum) (acid or heat) or treated (with either acid or heat) as the sole source of carbonaceous compounds. Additionally, nitrate was used in order to limit sulfate reduction and methanogenesis. Bacterial growth took place, as evidenced from 3 to 4 log unit increases in the 16S rRNA gene copy numbers as well as direct cell counts through three transfers on cleaned and reused substrate placed in fresh mineral medium. After 2 days, Aeromonas bestiarum-like organisms dominated the enrichments, irrespective of the substrate type. One month later, each substrate revealed major enrichments of organisms affiliated with different species of Clostridium. Moreover, only the heat-treated substrate selected Dysgonomonas capnocytophagoides-affiliated bacteria (Bacteroidetes). Towards the end of the experiment, members of the Proteobacteria (Aeromonas, Rhizobium and/or Serratia) became dominant in all three types of substrates. A total of 160 strains was isolated from the enrichments. Most of the strains tested (78%) were able to grow anaerobically on carboxymethyl cellulose and xylan. The final consortia yield attractive biological tools for the depolymerization of recalcitrant lignocellulosic materials and are proposed for the production of precursors of biofuels

Molecular Analysis of the Bacterial Communities in Crude Oil Samples from Two Brazilian Offshore Petroleum Platforms

Crude oil samples with high- and low-water content from two offshore platforms (PA and PB) in Campos Basin, Brazil, were assessed for bacterial communities by 16S rRNA gene-based clone libraries. RDP Classifier was used to analyze a total of 156 clones within four libraries obtained from two platforms. The clone sequences were mainly affiliated with Gammaproteobacteria (78.2% of the total clones); however, clones associated with Betaproteobacteria (10.9%), Alphaproteobacteria (9%), and Firmicutes (1.9%) were also identified. Pseudomonadaceae was the most common family affiliated with these clone sequences. The sequences were further analyzed by MOTHUR, yielding 81 operational taxonomic units (OTUs) grouped at 97% stringency. Richness estimators also calculated by MOTHUR indicated that oil samples with high-water content were the most diverse. Comparison of bacterial communities present in these four samples using LIBSHUFF and Principal Component Analysis (PCA) indicated that the water content significantly influenced the community structure only of crude oil obtained from PA. Differences between PA and PB libraries were observed, suggesting the importance of the oil field as a driver of community composition in this habitat

Classical properties of low-dimensional conductors: Giant capacitance and non-Ohmic potential drop

Electrical field arising around an inhomogeneous conductor when an electrical current passes through it is not screened, as distinct from 3D conductors, in low-dimensional conductors. As a result, the electrical field depends on the global distribution of the conductivity sigma(x) rather than on the local value of it, inhomogeneities of sigma(x) produce giant capacitances C(omega) that show frequency dependence at relatively low omega, and electrical fields develop in vast regions around the inhomogeneities of sigma(x). A theory of these phenomena is presented for 2D conductors.Comment: 5 pages, two-column REVTeX, to be published in Physical Review Letter

Macrostate Data Clustering

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral graph theory, dynamic eigenvectors, machine learning, macrostates, classificatio

Comparative Bioremediation of Crude Oil-Amended Tropical Soil Microcosms by Natural Attenuation, Bioaugmentation, or Bioenrichment

Bioremediation is an efficient strategy for cleaning up sites contaminated with organic pollutants. In this study, we evaluated the effectiveness of monitored natural attenuation, bioenrichment, and bioaugmentation using a consortium of three actinomycetes strains in remediating two distinct typical Brazilian soils from the Atlantic Forest and Cerrado biomes that were contaminated with crude oil, with or without the addition of NaCl. Microcosms were used to simulate bioremediation treatments over a 120-day period. During this period, we monitored total petroleum hydrocarbons (TPHs) and n-alkanes degradation and changes in bacterial communities. Over time, we found the degradation rate of n-alkanes was higher than TPH in both soils, independent of the treatment used. In fact, our data show that the total bacterial community in the soils was mainly affected by the experimental period of time, while the type of bioremediation treatment used was the main factor influencing the actinomycetes populations in both soils. Based on these data, we conclude that monitored natural attenuation is the best strategy for remediation of the two tropical soils studied, with or without salt addition

Electron spin operation by electric fields: spin dynamics and spin injection

Spin-orbit interaction couples electron spins to electric fields and allows electrical monitoring of electron spins and electrical detection of spin dynamics. Competing mechanisms of spin-orbit interaction are compared, and optimal conditions for the electric operation of electrons spins in a quantum well by a gate voltage are established. Electric spin injection into semiconductors is discussed with a special emphasis on the injection into ballistic microstructures. Dramatic effect of a long range Coulomb interaction on transport phenomena in space-quantized low-dimensional conductors is discussed in conclusion.Comment: A plenary paper at the 11th Intern. Conf. on Narrow Gap Semiconductors (Buffalo, NY, June 2003). To be published in Physica