We solve the dynamic Predecessor Problem with high probability (whp) in constant time, using only n bits of memory, for any constant δ > 0. The input keys are random wrt a wider class of the well studied and practically important class of (f1, f2)-smooth distributions introduced in [3]. It achieves O(1) whp amortized time. Its worst-case time is O( √ logn log logn ). Also, we prove whp O(log lo...

We solve the dynamic Predecessor Problem with high probability (whp) in constant time, using only n bits of memory, for any constant δ > 0. The input keys are random wrt a wider class of the well studied and practically important class of (f1, f2)-smooth distributions introduced in [3]. It achieves O(1) whp amortized time. Its worst-case time is O( √ logn log logn ). Also, we prove whp O(log lo...

We follow a research thread studying the predecessor problem on “smooth” distribution families. We propose a conceptually simpler solution utilizing wellknown results from much better studied variant of the problem that assumes nothing about the input. As a side effect, we are able to extend the range of handled input distributions for the most studied case needing expected O (log log n) time, ...

Given a set of n intervals representing an interval graph, the problem of finding a maximum matching between pairs of disjoint (nonintersecting) intervals has been considered in the sequential model. In this paper we present parallel algorithms for computing maximum cardinality matchings among pairs of disjoint intervals in interval graphs in the EREW PRAM and hypercube models. For the general ...

The present investigation sought to explore the relationship between learning styles and writing behaviors of EFL learners in a blended environment. It also aimed to identify the learning style types best predicting writing behaviors. Initially, the participants' preferred learning styles were identified through the Kolb’s learning style inventory (Kolb, 1984). Secondly, data were obtained thro...

There is an upsurge in interest in the Markov model and also more general stationary ergodic stochastic distributions in theoretical computer science community recently (e.g. see Vitter,Krishnan91], Karlin,Philips,Raghavan92], Raghavan92] for use of Markov models for on-line algorithms, e.g., cashing and prefetching). Their results used the fact that compressible sources are predictable (and vi...

In this paper we focus on the problem of designing very fast parallel algorithms for the convex hull and the vector maxima problems in three dimensions that are output-size sensitive. Our algorithms achieve Oðlog log n log hÞ parallel time and optimal Oðn log hÞ work with high probability in the CRCW PRAM where n and h are the input and output size, respectively. These bounds are independent of...

In this paper we explore the node complexity of recursive neural network implementations of frontier-to-root tree automata (FRA). Specifically, we show that an FRAO (Mealy version) with m states, l input-output labels, and maximum rank N can be implemented by a recursive neural network with O(radical(log l+log m)lm(N)/log l+N log m) units and four computational layers, i.e., without counting th...

Computing the Hermite Normal Form of an n n matrix using the best current algorithms typically requires O(n 3 log M) space, where M is a bound on the length of the columns of the input matrix. Although polynomial in the input size (which is O(n 2 log M)), this space blow-up can easily become a serious issue in practice when working on big integer matrices. In this paper we present a new algorit...

We prove that the average complexity, for the uniform distribution on complete deterministic automata, of Moore’s state minimization algorithm is O(n log log n), where n is the number of states in the input automata.