Experimental demonstration of near-infrared negative-index metamaterials

Metal-based negative refractive-index materials have been extensively studied in the microwave region. However, negative-index metamaterials have not been realized at near-IR or visible frequencies due to difficulties of fabrication and to the generally poor optical properties of metals at these wavelengths. In this Letter, we report the first fabrication and experimental verification of a transversely structured metal-dielectricmetal multilayer exhibiting a negative refractive index around 2 mu m. Both the amplitude and the phase of the transmission and reflection were measured experimentally, and are in good agreement with a rigorous coupled wave analysis

Incremental Medians via Online Bidding

In the k-median problem we are given sets of facilities and customers, and distances between them. For a given set F of facilities, the cost of serving a customer u is the minimum distance between u and a facility in F. The goal is to find a set F of k facilities that minimizes the sum, over all customers, of their service costs. Following Mettu and Plaxton, we study the incremental medians problem, where k is not known in advance, and the algorithm produces a nested sequence of facility sets where the kth set has size k. The algorithm is c-cost-competitive if the cost of each set is at most c times the cost of the optimum set of size k. We give improved incremental algorithms for the metric version: an 8-cost-competitive deterministic algorithm, a 2e ~ 5.44-cost-competitive randomized algorithm, a (24+epsilon)-cost-competitive, poly-time deterministic algorithm, and a (6e+epsilon ~ .31)-cost-competitive, poly-time randomized algorithm. The algorithm is s-size-competitive if the cost of the kth set is at most the minimum cost of any set of size k, and has size at most s k. The optimal size-competitive ratios for this problem are 4 (deterministic) and e (randomized). We present the first poly-time O(log m)-size-approximation algorithm for the offline problem and first poly-time O(log m)-size-competitive algorithm for the incremental problem. Our proofs reduce incremental medians to the following online bidding problem: faced with an unknown threshold T, an algorithm submits "bids" until it submits a bid that is at least the threshold. It pays the sum of all its bids. We prove that folklore algorithms for online bidding are optimally competitive.Comment: conference version appeared in LATIN 2006 as "Oblivious Medians via Online Bidding

Cooperative determination on cache replacement candidates for transcoding proxy caching

The original publication is available at www.springerlink.comTranscoding proxy caching is an important technology for improving the services over Internet, especially in the environment of mobile computing systems. In this paper, we address cooperative determination on cache replacement candidates for transcoding proxies. An original model which determines cache replacement candidates on all candidate nodes in a coordinated fashion with the objective of minimizing the total cost loss is proposed. We formulate this problem as an optimization problem and present a low-cost optimal solution for deciding cache replacement candidates.Keqiu Li, Hong Shen and Francis Y.L. Chi

A 5-approximation for capacitated facility location

In this paper, we propose and analyze a local search algorithm for the capacitated facility location problem. Our algorithm is a modification of the algorithm proposed by Zhang et al. [7] and improves the approximation ratio from 5.83 to 5. We achieve this by modifying the close, open and multi operations. The idea of taking linear combinations of inequalities used in Aggarwal et al [1] is crucial in achieving this result. The example proposed by Zhang et al. also shows that our analysis is tight

Collaborative Forwarding and Caching in Content Centric Networks

Part 1: Content-Centric NetworkingInternational audienceContent caching plays an important role in content-centric networks. The current design of content-centric networks adopts a limited, en-route hierarchical caching mechanism, and caching and forwarding are largely uncoordinated. In this paper, we propose a novel collaborative caching and forwarding design. In this design, collaboration is guided by content popularity ranking, based on which we introduce a collaborative forwarding table to allow coordination between caching and forwarding. We also propose a self-adaptive dual-segment cache division algorithm to deal with dynamic inconsistent content popularity. We evaluate our design via extensive simulations and demonstrate that our design improves content access cost and cache miss rate by at least 30% in a diverse network settings