Metric projection for dynamic multiplex networks

Evolving multiplex networks are a powerful model for representing the dynamics along time of different phenomena, such as social networks, power grids, biological pathways. However, exploring the structure of the multiplex network time series is still an open problem. Here we propose a two-steps strategy to tackle this problem based on the concept of distance (metric) between networks. Given a multiplex graph, first a network of networks is built for each time steps, and then a real valued time series is obtained by the sequence of (simple) networks by evaluating the distance from the first element of the series. The effectiveness of this approach in detecting the occurring changes along the original time series is shown on a synthetic example first, and then on the Gulf dataset of political events

2D fuzzy Anti-de Sitter space from matrix models

We study the fuzzy hyperboloids AdS^2 and dS^2 as brane solutions in matrix models. The unitary representations of SO(2,1) required for quantum field theory are identified, and explicit formulae for their realization in terms of fuzzy wavefunctions are given. In a second part, we study the (A)dS^2 brane geometry and its dynamics, as governed by a suitable matrix model. In particular, we show that trace of the energy-momentum tensor of matter induces transversal perturbations of the brane and of the Ricci scalar. This leads to a linearized form of Henneaux-Teitelboim-type gravity, illustrating the mechanism of emergent gravity in matrix models.Comment: 25 page

Budaya Organisasi Dalam Meningkatkan Kinerja Guru Pada SMA Negeri 1 Simeulue Timur

One of influencing factors in improving the teachers performance is organization culture. This study aims to know the discipline training pattern, the assumption of the importance of organization culture in improving the teachers performance and motivation, and inhibiting factors in improving the teachers performance at SMAN 1 East Simeulue of Simeulue sub-district. This study is qualitative descriptive approach. The technique of the data collection were through observations, interviews, and documentation. The subjects of the study were head master of the school, teachers, and school committee. The analysis of the research shows that (1) Fostering discipline in improving the teacher performance at SMAN 1 East Simeulue is by referring to the decided rules, whether both statutory regulations and school rules. The discipline approach was being conducted gradually started from decided job description, conducted persuasive approach, supervised the teaching-learning process, and guiding the teachers' task, and giving impose sanctions in accordance with the offense level. (2) A system of giving motivation to the teachers in improving their performance is being conducted through giving the unbinding, good service, career promotion, training opportunities, providing services on safety and convenience of the teacher task . (3) The inhibiting factor in improving the teacher performance is the lack of the school rule socialization, lack of coordination , in effective communication amongst personnel, and lack of involvement of the school committee in deciding the school policy

An introduction to spectral distances in networks (extended version)

Many functions have been recently defined to assess the similarity among networks as tools for quantitative comparison. They stem from very different frameworks - and they are tuned for dealing with different situations. Here we show an overview of the spectral distances, highlighting their behavior in some basic cases of static and dynamic synthetic and real networks

Sparse Predictive Structure of Deconvolved Functional Brain Networks

The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional structures between experimental conditions or groups shuns typical mass univariate methods. Furthermore most network estimation methods cannot distinguish between real and spurious correlation arising from the convolution due to nodes' interaction, which thus introduces additional noise in the data. We propose a machine learning pipeline aimed at identifying multivariate differences between brain networks associated to different experimental conditions. The pipeline (1) leverages the deconvolved individual contribution of each edge and (2) maps the task into a sparse classification problem in order to construct the associated "sparse deconvolved predictive network", i.e., a graph with the same nodes of those compared but whose edge weights are defined by their relevance for out of sample predictions in classification. We present an application of the proposed method by decoding the covert attention direction (left or right) based on the single-trial functional connectivity matrix extracted from high-frequency magnetoencephalography (MEG) data. Our results demonstrate how network deconvolution matched with sparse classification methods outperforms typical approaches for MEG decoding