On Dynamic Optimality for Binary Search Trees

Does there exist O(1)-competitive (self-adjusting) binary search tree (BST) algorithms? This is a well-studied problem. A simple offline BST algorithm GreedyFuture was proposed independently by Lucas and Munro, and they conjectured it to be O(1)-competitive. Recently, Demaine et al. gave a geometric view of the BST problem. This view allowed them to give an online algorithm GreedyArb with the same cost as GreedyFuture. However, no o(n)-competitive ratio was known for GreedyArb. In this paper we make progress towards proving O(1)-competitive ratio for GreedyArb by showing that it is O(\log n)-competitive

Maintaining Approximate Maximum Matching in an Incremental Bipartite Graph in Polylogarithmic Update Time

A sparse subgraph G\u27 of G is called a matching sparsifier if the size or weight of matching in G\u27 is approximately equal to the size or weight of maximum matching in G. Recently, algorithms have been developed to find matching sparsifiers in a static bipartite graph. In this paper, we show that we can find matching sparsifier even in an incremental bipartite graph. This observation leads to following results: 1. We design an algorithm that maintains a (1+epsilon) approximate matching in an incremental bipartite graph in O(log^2(n) / (epsilon^{4}) update time. 2. For weighted graphs, we design an algorithm that maintains (1+epsilon) approximate weighted matching in O((log(n)*log(n*N)) / (epsilon^4) update time where maxweight is the maximum weight of any edge in the graph

Asset Allocation and Predictability of Real Estate Returns

We examine the issue of optimal asset allocation among three broad classes of assetsÐÐLarge Stocks (proxied by the S&P composite index); real estate assets (a portfolio of thirty Equity Real Estate Investment Trusts (REITs) traded on major stock exchanges); and the risk-free asset (the one-month T-bill), employing the evidence on their predictability. An active strategy of investing in the assets, using predicted returns from our model outperforms investing in passive strategies, which are combinations of asset classes with fixed weights for the entire period of the study. Thus our superior performance is not due to diversification alone.

An Efficient Analytical Solution to Thwart DDoS Attacks in Public Domain

In this paper, an analytical model for DDoS attacks detection is proposed, in which propagation of abrupt traffic changes inside public domain is monitored to detect a wide range of DDoS attacks. Although, various statistical measures can be used to construct profile of the traffic normally seen in the network to identify anomalies whenever traffic goes out of profile, we have selected volume and flow measure. Consideration of varying tolerance factors make proposed detection system scalable to the varying network conditions and attack loads in real time. NS-2 network simulator on Linux platform is used as simulation testbed. Simulation results show that our proposed solution gives a drastic improvement in terms of detection rate and false positive rate. However, the mammoth volume generated by DDoS attacks pose the biggest challenge in terms of memory and computational overheads as far as monitoring and analysis of traffic at single point connecting victim is concerned. To address this problem, a distributed cooperative technique is proposed that distributes memory and computational overheads to all edge routers for detecting a wide range of DDoS attacks at early stage.Comment: arXiv admin note: substantial text overlap with arXiv:1203.240