On the Exploitation of Admittance Measurements for Wired Network Topology Derivation

The knowledge of the topology of a wired network is often of fundamental importance. For instance, in the context of Power Line Communications (PLC) networks it is helpful to implement data routing strategies, while in power distribution networks and Smart Micro Grids (SMG) it is required for grid monitoring and for power flow management. In this paper, we use the transmission line theory to shed new light and to show how the topological properties of a wired network can be found exploiting admittance measurements at the nodes. An analytic proof is reported to show that the derivation of the topology can be done in complex networks under certain assumptions. We also analyze the effect of the network background noise on admittance measurements. In this respect, we propose a topology derivation algorithm that works in the presence of noise. We finally analyze the performance of the algorithm using values that are typical of power line distribution networks.Comment: A version of this manuscript has been submitted to the IEEE Transactions on Instrumentation and Measurement for possible publication. The paper consists of 8 pages, 11 figures, 1 tabl

A dual output polarimeter devoted to the study of the Cosmic Microwave Background

We have developed a correlation radiometer at 33 GHz devoted to the search for residual polarization of the Cosmic Microwave Background (CMB). The two instruments`s outputs are linear combination of two Stokes Parameters (Q and U or U and V). The instrument is therefore directly sensitive to the polarized component of the radiation (respectively linear and circular). The radiometer has a beam-width oif 7 or 14 deg, but it can be coupled to a telescope increasing the resolution. The expected CMB polarization is at most a part per milion. The polarimeter has been designed to be sensitive to this faint signal, and it has been optimized to improve its long term stability, observing from the ground. In this contribution the performances of the instrument are presented, together with the preliminary test and observations.Comment: 12 pages, 6 figures, in print on the Proc. SPIE Conf. - August 200

A note on the integral equation for the Wilson loop in N = 2 D=4 superconformal Yang-Mills theory

We propose an alternative method to study the saddle point equation in the strong coupling limit for the Wilson loop in $\mathcal{N}=2$ D=4 super Yang-Mills with an SU(N) gauge group and 2N hypermultiplets. This method is based on an approximation of the integral equation kernel which allows to solve the simplified problem exactly. To determine the accuracy of this approximation, we compare our results to those obtained recently by Passerini and Zarembo. Although less precise, this simpler approach provides an explicit expression for the density of eigenvalues that is used to derive the planar free energy.Comment: 12 pages, v2: section 2.5 (Free Energy) amended and reference added, to appear in J. Phys.

Combining learning and constraints for genome-wide protein annotation

BackgroundThe advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale.ResultsWe present Ocelot, a predictive pipeline which simultaneously addresses functional and interaction annotation of all proteins of a given genome. The system combines sequence-based predictors for functional and protein-protein interaction (PPI) prediction with a consistency layer enforcing (soft) constraints as fuzzy logic rules. The enforced rules represent the available prior knowledge about the classification task, including taxonomic constraints over each GO hierarchy (e.g. a protein labeled with a GO term should also be labeled with all ancestor terms) as well as rules combining interaction and function prediction. An extensive experimental evaluation on the Yeast genome shows that the integration of prior knowledge via rules substantially improves the quality of the predictions. The system largely outperforms GoFDR, the only high-ranking system at the last CAFA challenge with a readily available implementation, when GoFDR is given access to intra-genome information only (as Ocelot), and has comparable or better results (depending on the hierarchy and performance measure) when GoFDR is allowed to use information from other genomes. Our system also compares favorably to recent methods based on deep learning

Bipartite quantum states and random complex networks

We introduce a mapping between graphs and pure quantum bipartite states and show that the associated entanglement entropy conveys non-trivial information about the structure of the graph. Our primary goal is to investigate the family of random graphs known as complex networks. In the case of classical random graphs we derive an analytic expression for the averaged entanglement entropy $\bar S$ while for general complex networks we rely on numerics. For large number of nodes $n$ we find a scaling $\bar{S} \sim c \log n +g_e$ where both the prefactor $c$ and the sub-leading O(1) term $g_e$ are a characteristic of the different classes of complex networks. In particular, $g_e$ encodes topological features of the graphs and is named network topological entropy. Our results suggest that quantum entanglement may provide a powerful tool in the analysis of large complex networks with non-trivial topological properties.Comment: 4 pages, 3 figure