37914727
Fractional Cascading Revisited
We present an alternative implementation of the fractional cascading data structure of Chazelle and Guibas (Algorithmica1 (1986), 133-162) that performs iterative search for a key in multiple ordered lists. The construction of our data structure uses randomization and simplifies the algorithm of Chazelle and Guibas, vastly making it practical to implement. Although our bounds are asymptotically similar to the earlier ones, there are improvements in the constant factors. Our analysis is novel and captures some of the inherent difficulties associated with the fractional cascading data structure. In particular, we use tools from branching process theory and derive some useful asymptotic bounds. The probability of deviation from the expected performance bounds decreases exponentially with number of keys. Also, under a restricted model, we obtain efficient bounds for updates in the data structure.
9302549
Planar Graph Blocking for External Searching

AbstractWe present a new scheme for storing a planar graph in external memory so that any online path can be traversed in an I-O efficient way. Our storage scheme significantly improves the previous results for planar graphs with bounded face size. We also prove an upper bound on I-O efficiency of any storage scheme for well-shaped triangulated meshes. For these meshes, our storage scheme achieves optimal performance.

32334601
Finding an Approximate Median with High Probability in Constant Parallel Time
Given n elements from a totally ordered set we outline a method for choosing an element whose rank is γn for some 0 0.
11169442
Linear Time Algorithms for Clustering Problems in Any Dimensions
We generalize the k-means algorithm presented by the authors [14] and show that the resulting algorithm can solve a larger class of clustering problems that satisfy certain properties (existence of a random sampling procedure and tightness). We prove these properties for the k-median and the discrete k-means clustering problems, resulting in O(2(k/e)O(1)dn) time (1+e)-approximation algorithms for these problems. These are the first algorithms for these problems linear in the size of the input (nd for n points in d dimensions), independent of dimensions in the exponent, assuming k and e to be fixed. A key ingredient of the k-median result is a (1+e)-approximation algorithm for the 1-median problem which has running time O(2(1/e)O(1)d). The previous best known algorithm for this problem had linear running time.
1068367
An Efficient Output-Size Sensitive Parallel Algorithm for Hidden-Surface Removal for Terrains
Abstract. We describe an efficient parallel algorithm for hidden-surface removal for terrain maps. The algorithm runs in O(log 4 n) steps on the CREW PRAM model with a work bound of O((n+k) \polylog ( n)) where n and k are the input and output sizes, respectively. In order to achieve the work bound we use a number of techniques, among which our use of persistent data structures is somewhat novel in the context of parallel algorithms.
10562059
Improved decremental algorithms for maintaining transitive closure and all-pairs shortest paths
We present improved algorithms for maintaining transitive closure and all-pairs shortest paths/distances in a digraph under deletion of edges.(MATH) For the problem of transitive closure, the previous best known algorithms, for achieving O(1) query time, require O(\min(m, \frac{n^3}{m}))$ amortized update time, implying an upper bound of O(n^{\frac{3}{2}})$ on update time per edge-deletion. We present an algorithm that achieves $O(1)$ query time and O(n \log^2n + \frac{n^2}{\sqrt{m}}{\sqrt{\log n}})$ update time per edge-deletion, thus improving the upper bound to O(n^{\frac{4}{3}}\sqrt[3]{\log n})$.(MATH) For the problem of maintaining all-pairs shortest distances in unweighted digraph under deletion of edges, we present an algorithm that requires O(\frac{n^3}{m} \log^2 n)$ amortized update time and answers a distance query in O(1) time. This improves the previous best known update bound by a factor of log n. For maintaining all-pairs shortest paths, we present an algorithm that achieves O(\min(n^{\frac{3}{2}} \sqrt{\log n}, \frac{n^3}{m} \log ^2n))$ amortized update time and reports a shortest path in optimal time (proportional to the length of the path). For the latter problem we improve the worst amortized update time bound by a factor of O(\sqrt{\frac{n}{\log n}})$.(MATH) We also present the first decremental algorithm for maintaining all-pairs (1+ε) approximate shortest paths/distances, for any ε > 0, that achieves a sub-quadratic update time of O(n log2n + \frac{n^2}{\sqrt{\epsilon m}}\sqrt{\log n})$ and optimal query time.Our algorithms are randomized and have one-sided error for query (with probability O(1/nc) for any constant c).
16723670
PLEASE: A Prototype Learning System Using Genetic Algorithms
Prototypes have been proposed as representation of concepts that are used eeec-tively by humans. Developing computational schemes for generating prototypes from examples , however, has proved to be a dii-cult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classiied training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space. We present experimental results on a series of artiicial problems of varying complexity. PLEASE performs competitively with several nearest neighbor clas-siication algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly diicult problems.
158365
Long-distance 802.11b links: performance measurements and experience
The use of 802.11 long-distance links is a cost-effective means of providing wireless connectivity to rural areas. Although deployments in this setting are increasing, a systematic study of the performance of 802.11 in these settings is lacking. The contributions of this paper are two-fold: (a)we present a detailed performance study of a set of long-distance 802.11b links at various layers of the network stack, and (b)we document the various non-obvious experiences during our study.Our study includes eight long-distance links, ranging from 1km to 37km in length. Unlike prior studies of outdoor 802.11 links, we find that the error rate as a function of the received signal strength behaves close to theory. Time correlation of any packet errors is negligible across a range of time-scales. We have observed at least one of the link to be robust to rain and fog. But any interference on the long-distance links can be detrimental to performance. Apart from this however, such long-distance links can be planned to work well with predictable performance. During our measurements, we have observed a few hardware/driver quirks as well as system bottlenecks apart from the wireless link itself. We believe that our measurements and the documentation of our experience will help future network planning as well as protocol design for these networks.
3543479
Influence of perspectives on help-giving behaviors
We have developed and analyzed probabilistic reciprocity schemes as strategies to be used by self-interested agents to decide on whether or not to help other agents [2]. The goal is to develop strategies to make self-interested agents cooperate. Such cooperation will improve the overall system performance when each cooperative act produces a net savings for the system(the helping agent incurs less cost than savings obtained by the helped agent). Probabilistic reciprocity strategies are considerably more sophisticated than deterministic reciprocity schemes like tit-for-tat [l] and avoids major problems associated with the latter schemes [3]. We have experimentally shown that the probabilistic reciprocity strategy leads to close to optimal individual and group performance, when agents interact repeatedly over a long period of time. This happens when group composition changes only slowly. Additionally, this strategy is stable against selfish intruders. In this paper, we analyze the problem of the use of the right perspective to calculate the balance of help even under complete information. In general, the cost(&) incurred by the helping agent will differ from the savings(Sh) obtained by the helped agent. Which of these values should be used to update the balance of help between the two agents? If the helping agent increase its balance of work with the helped agent by Sh, rather than by Ch, it can expect more help from the helped agent in future. But this approach can lead to ‘cancelations of help’ leading to no system-wide or individual benefits for cooper&ive behavior. The answer to this question fundamentally affects the emergence of cooperative behavior in self-interested agent groups.
16079000
Searching for optimal coalition structures
Coalition formation has been a very active area of research in multiagent systems. Most of this research has concentrated on decentralized procedures that allow self-interested agents to negotiate the formation of coalitions and division of coalition payoffs. A different line of research has addressed the problem of finding the optimal division of agents into coalitions such that the sum total of the the payoffs to all the coalitions is maximized (Larson and Sandholm, 1999). This is the optimal coalition structure identification problem. Deterministic search algorithms have been proposed and evaluated under the assumption that the performance of a coalition is independent of other coalitions. We use an order-based genetic algorithm (OBGA) as a stochastic search process to identify the optimal coalition structure. We compare the performance of the OBGA with a representative deterministic algorithm presented in the literature. Though the OBGA has no performance guarantees, it is found to dominate the deterministic algorithm in a significant number of problem settings. An additional advantage of the OBGA is its scalability to larger problem sizes and to problems where performance of a coalition depends on other coalitions in the environment.
16234197
Development of a wireless integrated toxic and explosive MEMS based gas sensor
Demand of strict environmental safety norms in mines, industries leads to the development of smart sensor systems for monitoring the explosive and toxic gases like methane (CH/sub 4/) and carbon monoxide (CO). In the present work a wireless integrated CH/sub 4/ and CO gas sensor with a unique transmission scheme has been proposed. It detects CH/sub 4/ & CO gas and generates frequency modulated (FM) signal in two distinct frequency bands in the range of 2.5823 GHz to 2.5542 GHz for CH/sub 4/ concentration from 1% to 2.5% in air and 2.4854 GHz to 2.404 GHz for CO concentration from 50ppm to 300ppm. Also it transmits the generated FM signal through antenna. The receiver section demodulates the signal and displays the gas concentration.