Polynomial-Time Approximation Scheme for Data Broadcast

The data broadcast problem is to find a schedule for broadcasting a given set of messages over multiple channels. The goal is to minimize the cost of the broadcast plus the expected response time to clients who periodically and probabilistically tune in to wait for particular messages. The problem models disseminating data to clients in asymmetric communication environments, where there is a much larger capacity from the information source to the clients than in the reverse direction. Examples include satellites, cable TV, internet broadcast, and mobile phones. Such environments favor the ``push-based'' model where the server broadcasts (pushes) its information on the communication medium and multiple clients simultaneously retrieve the specific information of individual interest. This paper presents the first polynomial-time approximation scheme (PTAS) for data broadcast with O(1) channels and when each message has arbitrary probability, unit length and bounded cost. The best previous polynomial-time approximation algorithm for this case has a performance ratio of 9/8

Insight on Behavior Management Competencies from the Field of Special Education

Previous research shows that a challenging area that keeps emerging for beginning teachers is maintaining order in the classroom or competent behavioral management skills. In special education, classroom management is complex. This extra layer of complexion is due to additional federal legislation including specific assessments and formal behavioral plans that must be completed on special education students. Studies note that shortages of special education teachers can be linked to behavioral management problems that cause burnout in the special education field. However, there is very limited research from special education practitioners and administrators in the field focusing on beginning behavior competencies. To fill this gap, a survey was completed by over 150 Michigan educators and administrators to identify those skills and practices necessary for effective behavioral management in the special education classroom. Questions included aspects of pre-service training, behaviors commonly seen in the classroom, as well as professional development opportunities available to practicing teachers. Statistical methods that were used include descriptive statistics, and ANOVAs. The results will inform principals, district administrators, and mentor teachers on the topics for continued professional development of first year special education teachers as well guide the training for the pre-service teacher

Glauber Dynamics on Trees and Hyperbolic Graphs

We study continuous time Glauber dynamics for random configurations with local constraints (e.g. proper coloring, Ising and Potts models) on finite graphs with $n$ vertices and of bounded degree. We show that the relaxation time (defined as the reciprocal of the spectral gap $|\lambda_1-\lambda_2|$) for the dynamics on trees and on planar hyperbolic graphs, is polynomial in $n$. For these hyperbolic graphs, this yields a general polynomial sampling algorithm for random configurations. We then show that if the relaxation time $\tau_2$ satisfies $\tau_2=O(1)$, then the correlation coefficient, and the mutual information, between any local function (which depends only on the configuration in a fixed window) and the boundary conditions, decays exponentially in the distance between the window and the boundary. For the Ising model on a regular tree, this condition is sharp.Comment: To appear in Probability Theory and Related Field

The reverse greedy algorithm for the metric k-median problem

The Reverse Greedy algorithm (RGreedy) for the k-median problem works as follows. It starts by placing facilities on all nodes. At each step, it removes a facility to minimize the resulting total distance from the customers to the remaining facilities. It stops when k facilities remain. We prove that, if the distance function is metric, then the approximation ratio of RGreedy is between ?(log n/ log log n) and O(log n).Comment: to appear in IPL. preliminary version in COCOON '0

The Maximum Size of Dynamic Data Structures

This paper develops two probabilistic methods that allow the analysis of the maximum data structure size encountered during a sequence of insertions and deletions in data structures such as priority queues, dictionaries, linear lists, and symbol tables, and in sweepline structures for geometry and Very-Large-Scale-Integration (VLSI) applications. The notion of the "maximum" is basic to issues of resource preallocation. The methods here are applied to combinatorial models of file histories and probabilistic models, as well as to a non-Markovian process (algorithm) for processing sweepline information in an efficient way, called "hashing with lazy deletion" (HwLD). Expressions are derived for the expected maximum data structure size that are asymptotically exact, that is, correct up to lower-order terms; in several cases of interest the expected value of the maximum size is asymptotically equal to the maximum expected size. This solves several open problems, including longstanding questions in queueing theory. Both of these approaches are robust and rely upon novel applications of techniques from the analysis of algorithms. At a high level, the first method isolates the primary contribution to the maximum and bounds the lesser effects. In the second technique the continuous-time probabilistic model is related to its discrete analog--the maximum slot occupancy in hashing

Data structures maxima

Projet EURECAThe purpose of this paper is to analyze the maxima properties (value and position) of some data structures. Our theorems concern the distribution of the random variables. Previously known results usually dealt with the mean and sometimes the variance of these random variables. Many of our results rely on diffusion techniques. That is a very powerful tool, which has already been used with some success in the analysis of algorithms

The estimated burden of fungal disease in South Africa

Publication fees were paid via funding from a grant from Fonds.Peer reviewedPublisher PD