Graph measures and network robustness

Network robustness research aims at finding a measure to quantify network robustness. Once such a measure has been established, we will be able to compare networks, to improve existing networks and to design new networks that are able to continue to perform well when it is subject to failures or attacks. In this paper we survey a large amount of robustness measures on simple, undirected and unweighted graphs, in order to offer a tool for network administrators to evaluate and improve the robustness of their network. The measures discussed in this paper are based on the concepts of connectivity (including reliability polynomials), distance, betweenness and clustering. Some other measures are notions from spectral graph theory, more precisely, they are functions of the Laplacian eigenvalues. In addition to surveying these graph measures, the paper also contains a discussion of their functionality as a measure for topological network robustness

Infrared induced visible emission from porous silicon: the mechanism of anodic oxidatio

The visible luminescence caused by anodic oxidation of p-type porous silicon has been studied. It is shown that similar luminescence can be observed in n-type material by illumination with near-infrared light. Addition of a suitable reducing agent to the electrolyte solution can both suppress the oxidation of the porous layer and quench its luminescence. These results confirm a previously suggested mechanism, in which the capture of a valence band hole in a surface bond of the porous semiconductor gives rise to a surface state intermediate capable of thermally injecting an electron into the conduction band.\ud \u

Reduction of Peroxodisulfate at Porous and Crystalline Silicon Electrodes: An Anomaly\ud

Electroluminescence from n-type porous silicon can be generated in solution by reduction of peroxodisulfate. It has been assumed that the SO4•- radical ion, formed in the first reduction step, injects a hole into the valence band of the porous semiconductor. The hole should subsequently undergo radiative recombination with a conduction band electron. Using two techniques, viz., photocurrent quantum efficiency measurements with p-type porous and crystalline silicon electrodes and minority carrier injection studies with the “transistor technique”, we found that the reduction of peroxodisulfate is, however, not always accompanied by hole injection. The silicon results are compared with results obtained on GaAs electrodes. \u

The Minimal Spectral Radius of Graphs with a Given Diameter

AMS classsifications: 05C50; 05E99; 94C15;graphs;spectral radius;diameter;networks;virus propagation

Photoselective Metal Deposition on Amorphous Silicon p-i-n Solar Cells\ud

A novel method is described for the patternwise metallization of amorphous silicon solar cells, based on photocathodic deposition. The electric field of the p-i-n structure is used for the separation of photogenerated charge carriers. The electrons are driven to the interface of the n+-layer with the solution where they reduce metal ions to metal. The large difference between the conductivity of dark and illuminated areas and the high sheet resistance of the n-type layer makes it possible to define a metal pattern by selective illumination. It is shown that both nickel and gold patterns can be deposited using this method. After annealing, an ohmic nickel contact is formed and the cell exhibits good photovoltaic characteristics

ELASTICITY: Topological Characterization of Robustness in Complex Networks

Just as a herd of animals relies on its robust social structure to survive in the wild, similarly robustness is a crucial characteristic for the survival of a complex network under attack. The capacity to measure robustness in complex networks defines the resolve of a network to maintain functionality in the advent of classical component failures and at the onset of cryptic malicious attacks. To date, robustness metrics are deficient and unfortunately the following dilemmas exist: accurate models necessitate complex analysis while conversely, simple models lack applicability to our definition of robustness. In this paper, we define robustness and present a novel metric, elasticity- a bridge between accuracy and complexity-a link in the chain of network robustness. Additionally, we explore the performance of elasticity on Internet topologies and online social networks, and articulate results

Analysis of rotational coupling in collisions of Li+ with Ne leading to double excitation of Ne \ud

Electron angular distributions due to autoionization of Ne, doubly excited to the (2p43s2)1D state in collisions with Li+ in the energy range 1.2-2.2 keV, are measured in coincidence with Li+ scattered into a well defined direction ( Phi =0 degrees , Theta cm=10.8 degrees ). The experimental findings are analysed with the help of a collision model proposed earlier. In this model the initial excitation occurs by radial diabatic coupling to a molecular Sigma -state at small distances, followed by rotational coupling to Pi - and Delta -states at intermediate distances in the second half of the collision. The energy splitting between the Sigma -, Pi - and Delta -states is described by a model function. By adapting two parameters of this model function, the experimental findings can be reproduced within the experimental error in numerical calculations involving the relevant set of coupled differential equations. \u

Action classification using a discriminative non-parametric hidden Markov model

We classify human actions occurring in videos, using the skeletal joint positions extracted from a depth image sequence as features. Each action class is represented by a non-parametric Hidden Markov Model (NP-HMM) and the model parameters are learnt in a discriminative way. Specifically, we use a Bayesian framework based on Hierarchical Dirichlet Process (HDP) to automatically infer the cardinality of hidden states and formulate a discriminative function based on distance between Gaussian distributions to improve classification performance. We use elliptical slice sampling to efficiently sample parameters from the complex posterior distribution induced by our discriminative likelihood function. We illustrate our classification results for action class models trained using this technique