CW lasing in a telecom fibre due to the random distributed feedback via Rayleigh scattering

We demonstrate a fibre laser with a mirrorless cavity that operates via Rayleigh scattering amplified through the Raman effect. The properties of such random distributed feedback laser appear different from those of both traditional random lasers and conventional fibre lasers. ©2010 IEEE.

Statistical properties of radiation of multiwavelength random DFB fiber laser

In the presented paper, the temporal and statistical properties of a Lyot filter based multiwavelength random DFB fiber laser with a wide flat spectrum, consisting of individual lines, were investigated. It was shown that separate spectral lines forming the laser spectrum have mostly Gaussian statistics and so represent stochastic radiation, but at the same time the entire radiation is not fully stochastic. A simple model, taking into account phenomenological correlations of the lines' initial phases was established. Radiation structure in the experiment and simulation proved to be different, demanding interactions between different lines to be described via a NLSE-based model.

Disorder-induced light localisation:from random to artificial

Light localisation in one-dimensional (1D) randomly disordered medium is usually characterized by randomly distributed resonances with fluctuating transmission values, instead of selectively distributed resonances with close-to-unity transmission values that are needed in real application fields. By a resonance tuning scheme developed recently, opening of favorable resonances or closing of unfavorable resonances are achieved by disorder micro-modification, both on the layered medium and the fibre Bragg grating (FBG) array. And furthermore, it is shown that those disorder-induced resonances are independently tunable. Therefore, selected resonances and arranged light localisation can be achieved via artificial disorder, and thus meet the demand of various application fields.

Random walk with restart over dynamic graphs

Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|^3) time and O(|V|^2) memory to compute all (|V|^2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|^3) to O(|delta|) time for each update without loss of accuracy, where |delta| (<<|V|^2) is the number of affected proximities. (2) To avoid O(|V|^2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) memory and O(|V|/l) I/O costs, where 1<=l<=|V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1–2 orders of magnitude faster than other competitors while securing scalability and exactness.