DePaul University

About: DePaul University is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 5658 authors who have published 11562 publications receiving 295257 citations.

Parametric Duality and Kernelization: Lower Bounds and Upper Bounds on Kernel Size

Abstract: Determining whether a parameterized problem is kernelizable and has a small kernel size has recently become one of the most interesting topics of research in the area of parameterized complexity and algorithms. Theoretically, it has been proved that a parameterized problem is kernelizable if and only if it is fixed-parameter tractable. Practically, applying a data reduction algorithm to reduce an instance of a parameterized problem to an equivalent smaller instance (i.e., a kernel) has led to very efficient algorithms and now goes hand-in-hand with the design of practical algorithms for solving $\mathcal{NP}$-hard problems. Well-known examples of such parameterized problems include the vertex cover problem, which is kernelizable to a kernel of size bounded by $2k$, and the planar dominating set problem, which is kernelizable to a kernel of size bounded by $335k$. In this paper we develop new techniques to derive upper and lower bounds on the kernel size for certain parameterized problems. In terms of our lower bound results, we show, for example, that unless $\mathcal{P} = \mathcal{NP}$, planar vertex cover does not have a problem kernel of size smaller than $4k/3$, and planar independent set and planar dominating set do not have kernels of size smaller than $2k$. In terms of our upper bound results, we further reduce the upper bound on the kernel size for the planar dominating set problem to $67 k$, improving significantly the $335 k$ previous upper bound given by Alber, Fellows, and Niedermeier [J. ACM, 51 (2004), pp. 363-384]. This latter result is obtained by introducing a new set of reduction and coloring rules, which allows the derivation of nice combinatorial properties in the kernelized graph leading to a tighter bound on the size of the kernel. The paper also shows how this improved upper bound yields a simple and competitive algorithm for the planar dominating set problem.

Trait Goal Orientation, Self-Regulation, and Performance: A Meta-Analysis

Abstract: Purpose The purpose of this paper is to meta-analytically examine trait goal orientation constructs and their relationships with the self-regulation variables of self-monitoring, self-evaluations, self-reactions, and self-efficacy as well as task performance across a range of contexts.

Characterization of the theorectical radiation dose enhancement from nanoparticles.

Abstract: Recently, nanoparticles have been considered as a method of providing radiation dose enhancement in tumors. In order to quantify this affect, a dose enhancement factor (DEF) is defined that represents the ratio of the dose deposited in tumor with nanoparticles, divided by the dose deposited in the tumor without nanoparticles. Materials with atomic numbers (Z) ranging from 25 to 90 are considered in this analysis. In addition, the energy spectrum for a number of external beam x-ray sources and common radionuclides are evaluated. For a nanoparticle concentration of 5 mg/ml, the DEF is 70), the DEF increases and is a maximum for the highest Z materials. High atomic number nanoparticles coupled with low energy external beam x-rays or brachytherapy sources offer the potential of significantly enhancing the delivered dose.

Modeling and verification of IPSec and VPN security policies

Abstract: IPSec has become the defacto standard protocol for secure Internet communications, providing traffic integrity, confidentiality and authentication. Although IPSec supports a rich set of protection modes and operations, its policy configuration remains a complex and error-prone task. The complex semantics of IP Sec policies that allow for triggering multiple rule actions with different security modes/operations coordinated between different IPSec gateways in the network increases significantly the potential of policy misconfiguration and thereby insecure transmission. Successful deployment of IPSec requires thorough and automated analysis of the policy configuration consistency for IPSec devices across the entire network. In this paper, we present a generic model that captures various filtering policy semantics using Boolean expressions. We use this model to derive a canonical representation for IPSec policies using ordered binary decision diagrams. Based on this representation, we develop a comprehensive framework to classify and identify conflicts that could exist in a single IPSec device (intra-policy conflicts) or between different IPSec devices (inter-policy conflicts) in enterprise networks. Our testing and evaluation study on different network environments demonstrates the effectiveness and efficiency of our approach.

On-demand feature recommendations derived from mining public product descriptions

Abstract: We present a recommender system that models and recommends product features for a given domain. Our approach mines product descriptions from publicly available online specifications, utilizes text mining and a novel incremental diffusive clustering algorithm to discover domain-specific features, generates a probabilistic feature model that represents commonalities, variants, and cross-category features, and then uses association rule mining and the k-Nearest-Neighbor machine learning strategy to generate product specific feature recommendations. Our recommender system supports the relatively labor-intensive task of domain analysis, potentially increasing opportunities for re-use, reducing time-to-market, and delivering more competitive software products. The approach is empirically validated against 20 different product categories using thousands of product descriptions mined from a repository of free software applications.