Undamped Cooper pairs in a non-degenerate electron system

Following the reaction matrix technique and the Kanamori approximation. a condition is obtained for the occurence of undamped Cooper pairs in a non-degenerate electron system. Its relevance to induced superconductivity in systems with artificially populated (optically pumped) bands is pointed out.

Phenomenological aspects of phase fluctuation in high Tc superconductors: Cooper pair droplets

We point out how fluctuation of the phase of the superconducting order parameter can play a key role in our understanding of high Te superconductors. A simple universal criterion is given which illustrates why all oxide superconductors in contrast to classical superconductors ought to behave as a lattice of cooper pairs. T-c is to be thought of as the temperature of phase coherence or the temperature above which the lattice of Cooperpair 'melts' into a phase of Cooper-pair droplets that starts forming at T approximate to T-* . This is the pseudo-gap region. Quantum fluctuation of the phase predicts a superconductor to insulator phase transition for all underdoped materials.

Faster Algorithms for Incremental Topological Ordering.Robert Tarjan

We present two online algorithms for maintaining a topological order of a directed acyclic graph as arcs are added, and detecting a cycle when one is created. Our first algorithm takes O(m 1/2) amortized time per arc and our second algorithm takes O(n 2.5/m) amortized time per arc, where n is the number of vertices and m is the total number of arcs. For sparse graphs, our O(m 1/2) bound improves the best previous bound by a factor of logn and is tight to within a constant factor for a natural class of algorithms that includes all the existing ones. Our main insight is that the two-way search method of previous algorithms does not require an ordered search, but can be more general, allowing us to avoid the use of heaps (priority queues). Instead, the deterministic version of our algorithm uses (approximate) median-finding; the randomized version of our algorithm uses uniform random sampling. For dense graphs, our O(n 2.5/m) bound improves the best previously published bound by a factor of n 1/4 and a recent bound obtained independently of our work by a factor of logn. Our main insight is that graph search is wasteful when the graph is dense and can be avoided by searching the topological order space instead. Our algorithms extend to the maintenance of strong components, in the same asymptotic time bounds.