13,272 research outputs found

    Partial fillup and search time in LC tries

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    Andersson and Nilsson introduced in 1993 a level-compressed trie (in short: LC trie) in which a full subtree of a node is compressed to a single node of degree being the size of the subtree. Recent experimental results indicated a 'dramatic improvement' when full subtrees are replaced by partially filled subtrees. In this paper, we provide a theoretical justification of these experimental results showing, among others, a rather moderate improvement of the search time over the original LC tries. For such an analysis, we assume that n strings are generated independently by a binary memoryless source with p denoting the probability of emitting a 1. We first prove that the so called alpha-fillup level (i.e., the largest level in a trie with alpha fraction of nodes present at this level) is concentrated on two values with high probability. We give these values explicitly up to O(1), and observe that the value of alpha (strictly between 0 and 1) does not affect the leading term. This result directly yields the typical depth (search time) in the alpha-LC tries with p not equal to 1/2, which turns out to be C loglog n for an explicitly given constant C (depending on p but not on alpha). This should be compared with recently found typical depth in the original LC tries which is C' loglog n for a larger constant C'. The search time in alpha-LC tries is thus smaller but of the same order as in the original LC tries.Comment: 13 page

    The oscillatory distribution of distances in random tries

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    We investigate \Delta_n, the distance between randomly selected pairs of nodes among n keys in a random trie, which is a kind of digital tree. Analytical techniques, such as the Mellin transform and an excursion between poissonization and depoissonization, capture small fluctuations in the mean and variance of these random distances. The mean increases logarithmically in the number of keys, but curiously enough the variance remains O(1), as n\to\infty. It is demonstrated that the centered random variable \Delta_n^*=\Delta_n-\lfloor2\log_2n\rfloor does not have a limit distribution, but rather oscillates between two distributions.Comment: Published at http://dx.doi.org/10.1214/105051605000000106 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Analysis of Steiner subtrees of Random Trees for Traceroute Algorithms

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    We consider in this paper the problem of discovering, via a traceroute algorithm, the topology of a network, whose graph is spanned by an infinite branching process. A subset of nodes is selected according to some criterion. As a measure of efficiency of the algorithm, the Steiner distance of the selected nodes, i.e. the size of the spanning sub-tree of these nodes, is investigated. For the selection of nodes, two criteria are considered: A node is randomly selected with a probability, which is either independent of the depth of the node (uniform model) or else in the depth biased model, is exponentially decaying with respect to its depth. The limiting behavior the size of the discovered subtree is investigated for both models

    Training a perceptron in a discrete weight space

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    On-line and batch learning of a perceptron in a discrete weight space, where each weight can take 2L+12 L+1 different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous perceptron and prediction following the clipped weights. The learning is described by a new set of order parameters, composed of the overlaps between the teacher and the continuous/clipped students. Different scenarios are examined among them on-line learning with discrete/continuous transfer functions and off-line Hebb learning. The generalization error of the clipped weights decays asymptotically as exp(Kα2)exp(-K \alpha^2)/exp(eλα)exp(-e^{|\lambda| \alpha}) in the case of on-line learning with binary/continuous activation functions, respectively, where α\alpha is the number of examples divided by N, the size of the input vector and KK is a positive constant that decays linearly with 1/L. For finite NN and LL, a perfect agreement between the discrete student and the teacher is obtained for αLln(NL)\alpha \propto \sqrt{L \ln(NL)}. A crossover to the generalization error 1/α\propto 1/\alpha, characterized continuous weights with binary output, is obtained for synaptic depth L>O(N)L > O(\sqrt{N}).Comment: 10 pages, 5 figs., submitted to PR

    Asymptotic genealogy of a critical branching process

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    Consider a continuous-time binary branching process conditioned to have population size n at some time t, and with a chance p for recording each extinct individual in the process. Within the family tree of this process, we consider the smallest subtree containing the genealogy of the extant individuals together with the genealogy of the recorded extinct individuals. We introduce a novel representation of such subtrees in terms of a point-process, and provide asymptotic results on the distribution of this point-process as the number of extant individuals increases. We motivate the study within the scope of a coherent analysis for an a priori model for macroevolution.Comment: 30 page
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