A Deterministic Algorithm for the Vertex Connectivity Survivable Network Design Problem

In the vertex connectivity survivable network design problem we are given an undirected graph G = (V,E) and connectivity requirement r(u,v) for each pair of vertices u,v. We are also given a cost function on the set of edges. Our goal is to find the minimum cost subset of edges such that for every pair (u,v) of vertices we have r(u,v) vertex disjoint paths in the graph induced by the chosen edges. Recently, Chuzhoy and Khanna presented a randomized algorithm that achieves a factor of O(k^3 log n) for this problem where k is the maximum connectivity requirement. In this paper we derandomize their algorithm to get a deterministic O(k^3 log n) factor algorithm. Another problem of interest is the single source version of the problem, where there is a special vertex s and all non-zero connectivity requirements must involve s. We also give a deterministic O(k^2 log n) algorithm for this problem

Preclinical discovery of duloxetine for the treatment of depression

Introduction: Affective disorders, including major depressive disorder (MDD), are among the most severely disabling mental disorders, and in many cases areIntroduction: Affective disorders, including major depressive disorder (MDD), are among the most severely disabling mental disorders, and in many cases are associated with poor treatment outcomes. From the emergence of the monoamine hypothesis of depression, the first-line treatment for MDD had mainly acted by inhibiting monoamine reuptake, and thereby increasing these levels in the synaptic cleft. However, in recent years, several newantidepressant drugs have appeared, including duloxetine, a dual serotonin (5-HT) and noradrenaline (NA) reuptake inhibitor recommended for the treatment of MDD. Areas covered: The article reviews and discusses the biochemical and functional profile of duloxetine splitting the review into acute and long-term treatment with this dual monoamine reuptake inhibitor. In addition, the authors summarize available preclinical behavioral research data, which have demonstrated among other effects, the antidepressant-like activity of duloxetine in several animal models. The authors focus on the most recent literature on synaptic neuroplasticity modulation of this antidepressant drug. Finally, the authors briefly mention other approved indications of duloxetine. Expert opinion: Duloxetine inhibits 5-HT and NA reuptake, effectively desensitizes various autoreceptors and promotes neuroplasticity. Clinically, duloxetine is an effective antidepressant that is well tolerated and has significant efficacy in the treatment of MDD. associated with poor treatment outcomes. From the emergence of the monoamine hypothesis of depression, the first-line treatment for MDD had mainly acted by inhibiting monoamine reuptake, and thereby increasing these levels in the synaptic cleft. However, in recent years, several new antidepressant drugs have appeared, including duloxetine, a dual serotonin (5-HT) and noradrenaline (NA) reuptake inhibitor recommended for the treatment of MDD

Interplay between Josephson and Aharonov-Bohm effects in Andreev interferometers

Proximity induced quantum coherence of electrons in multi-terminal voltage-driven hybrid normal-superconducting nanostructures may result in a non-trivial interplay between topology-dependent Josephson and Aharonov-Bohm effects. We elucidate a trade-off between stimulation of the voltage-dependent Josephson current due to non-equilibrium effects and quantum dephasing of quasiparticles causing reduction of both Josephson and Aharonov-Bohm currents. We also predict phase-shifted quantum coherent oscillations of the induced electrostatic potential as a function of the externally applied magnetic flux. Our results may be employed for engineering superconducting nanocircuits with controlled quantum properties

Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective

This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex optimization for signal processin