A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabasi-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists.

Application of the Z-scan technique to determine the optical Kerr coefficient and two-photon absorption coefficient of magnetite nanoparticles colloidal suspension

We investigate the occurrence of the optical Kerr effect and two-photon absorption when an oil-based magnetic Fe3O4 nanoparticles colloidal suspension is illuminated with high intensity femtosecond laser pulses. The frequency of the pulses is controlled and the Z-scan technique is employed in our measurements of the nonlinear optical Kerr coefficient (n(2)) and two-photon absorption coefficient (beta). From these values it was possible to calculate the real and imaginary parts of the third-order susceptibility. We observed that increasing the pulse frequency, additional physical processes take place, increasing artificially the absolute values of n(2) and beta. The experimental conditions are discussed to assure the obtention of reliable values of these nonlinear optical parameters, which may be useful in all-optical switching and optical power limiting applications. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4723829]

Femtosecond third-order nonlinear spectra of lead-germanium oxide glasses containing silver nanoparticles

This work reports on the spectral dependence of both nonlinear refraction and absorption in lead-germanium oxide glasses (PbO-GeO2) containing silver nanoparticles. We have found that this material is suitable for all-optical switching at telecom wavelengths but at the visible range it behaves either as a saturable absorber or as an optical limiter. (C) 2012 Optical Society of America

Efficient 1535 nm light emission from an all-Si-based optical micro-cavity containing Er3+ and Yb3+ ions

This work reports on the construction and spectroscopic analyses of optical micro-cavities (OMCs) that efficiently emit at ~1535 nm. The emission wavelength matches the third transmission window of commercial optical fibers and the OMCs were entirely based on silicon. The sputtering deposition method was adopted in the preparation of the OMCs, which comprised two Bragg reflectors and one spacer layer made of either Er- or ErYb-doped amorphous silicon nitride. The luminescence signal extracted from the OMCs originated from the 4I13/2→4I15/2 transition (due to Er3+ ions) and its intensity showed to be highly dependent on the presence of Yb3+ ions.According to the results, the Er3+-related light emission was improved by a factor of 48 when combined with Yb3+ ions and inserted in the spacer layer of the OMC. The results also showed the effectiveness of the present experimental approach in producing Si-based light-emitting structures in which the main characteristics are: (a) compatibility with the actual microelectronics industry, (b) the deposition of optical quality layers with accurate composition control, and (c) no need of uncommon elements-compounds nor extensive thermal treatments. Along with the fundamental characteristics of the OMCs, this work also discusses the impact of the Er3+-Yb3+ ion interaction on the emission intensity as well as the potential of the present findings.

A simple-versatile approach to achieve all-Si-based optical micro-cavities

At present, solid thin films are recognized by their well established and mature processing technology that is able to produce components which, depending on their main characteristics, can perform either passive or active functions. Additionally, Si-based materials in the form of thin films perfectly match the concept of miniaturized and low-consumption devices-as required in various modern technological applications. Part of these aspects was considered in the present work that was concerned with the study of optical micro-cavities entirely based on silicon and silicon nitride thin films. The structures were prepared by the sputtering deposition method which, due to the adopted conditions (atmosphere and deposition rate) and arrangement of layers, provided cavities operating either in the visible (at ~ 670 nm) or in the near-infrared (at ~ 1560 nm) wavelength ranges. The main differential of the work relies on the construction of optical microcavities with a reduced number of periods whose main properties can be changed by thermal annealing treatments. The work also discusses the angle-dependent behavior of the optical transmission profiles as well as the use of the COMSOL software package to simulate the microcavities.

Tailoring the structure, composition, optical properties and catalytic activity of Ag-Au nanoparticles by the galvanic replacement reaction

This Letter reports on the synthesis of Ag-Au nanoparticles (NPs) with controlled structures and compositions via a galvanic replacement reaction between Ag NPs and AuCl4(aq)- followed by the investigation of their optical and catalytic properties. Our results showed the formation of porous walls, hollow interiors and increased Au content in the Ag-Au NPs as the volume of AuCl4(aq)- employed in the reaction was increased. These variations led to a red shift and broadening of the SPR peaks and an increase of up to 10.9-folds in the catalytic activity towards the reduction of 4-nitrophenol relative to Ag NPs. (C) 2012 Elsevier B.V. All rights reserved.

Temperature Effect on the Two-Photon Absorption Spectrum of All-trans-beta-carotene

In this report, we investigate the influence of temperature on the two-photon absorption (2PA) spectrum of all-trans-beta-carotene using the femtosecond white-light-continuum Z-scan technique. We observed that the 2PA cross-section decreases quadratically with the temperature. Such effect was modeled using a three-energy-level diagram within the sum-over-essential states approach, assuming temperature dependencies to the transition dipole moment and refractive index of the solvent. The results show that the transition dipole moments from ground to excited state and between the excited states, which governed the two-photon matrix element, have distinct behaviors with the temperature. The first one presents a quadratic dependence, while the second exhibits a linear dependence. Such effects were attributed mainly to the trans -> cis thermal interconversion process, which decreases the effective conjugation length, contributing to diminishing the transition dipole moments and, consequently, the 2PA cross-section.

Discriminating Different Classes of Biological Networks by Analyzing the Graphs Spectra Distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e. g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Disentangling social networks from spatiotemporal dynamics: the temporal structure of a dolphin society

Social networks are static illustrations of dynamic societies, within which social interactions are constantly changing. Fundamental sources of variation include ranging behaviour and temporal demographic changes. Spatiotemporal dynamics can favour or limit opportunities for individuals to interact, and then a network may not essentially represent social processes. We examined whether a social network can embed such nonsocial effects in its topology, whereby emerging modules depict spatially or temporally segregated individuals. To this end, we applied a combination of spatial, temporal and demographic analyses to a long-term study of the association patterns of Guiana dolphins, Sotalia guianensis. We found that association patterns are organized into a modular social network. Space use was unlikely to reflect these modules, since dolphins' ranging behaviour clearly overlapped. However, a temporal demographic turnover, caused by the exit/entrance of individuals (most likely emigration/immigration), defined three modules of associations occurring at different times. Although this factor could mask real social processes, we identified the temporal scale that allowed us to account for these demographic effects. By looking within this turnover period (32 months), we assessed fission-fusion dynamics of the poorly known social organization of Guiana dolphins. We highlight that spatiotemporal dynamics can strongly influence the structure of social networks. Our findings show that hypothetical social units can emerge due to the temporal opportunities for individuals to interact. Therefore, a thorough search for a satisfactory spatiotemporal scale that removes such nonsocial noise is critical when analysing a social system. (C) 2012 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Architectures, stability and optimization for clock distribution networks

Synchronous telecommunication networks, distributed control systems and integrated circuits have its accuracy of operation dependent on the existence of a reliable time basis signal extracted from the line data stream and acquirable to each node. In this sense, the existence of a sub-network (inside the main network) dedicated to the distribution of the clock signals is crucially important. There are different solutions for the architecture of the time distribution sub-network and choosing one of them depends on cost, precision, reliability and operational security. In this work we expose: (i) the possible time distribution networks and their usual topologies and arrangements. (ii) How parameters of the network nodes can affect the reachability and stability of the synchronous state of a network. (iii) Optimizations methods for synchronous networks which can provide low cost architectures with operational precision, reliability and security. (C) 2011 Elsevier B. V. All rights reserved.

Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

Structural refinement, optical and microwave dielectric properties of BaZrO3

In this work, barium zirconate (BaZrO3) ceramics synthesized by solid state reaction method and sintered at 1670 degrees C for 4 h were characterized by X-ray diffraction (XRD), Rietveld refinement, and Fourier transform infrared (FT-IR) spectroscopy. XRD patterns, Rietveld refinement data and FT-IR spectra which confirmed that BaZrO3 ceramics have a perovskite-type cubic structure. Optical properties were investigated by ultraviolet-visible (UV-vis) absorption and photoluminescence (PL) measurements. UV-vis absorption spectra suggested an indirect allowed transition with the existence of intermediary energy levels within the band gap. Intense visible green PL emission was observed in BaZrO3 ceramics upon excitation with a 350 nm wavelength. This behavior is due to a majority of deep defects within the band gap caused by symmetry breaking in octahedral [ZrO6] clusters in the lattice. The microwave dielectric constant and quality factor were measured using the method proposed by Hakki-Coleman. The dielectric resonator antenna (DRA) was investigated experimentally and numerically using a monopole antenna through an infinite ground plane and Ansoft's high frequency structure simulator software, respectively. The required resonance frequency and bandwidth of DRA were investigated by adjusting the dimension of the same material. (C) 2011 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Extractive summarization using complex networks and syntactic dependency

The realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general. (C) 2011 Elsevier B.V. All rights reserved.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.