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.

Efficient scheduling of nonuniform packet traffic in a WDM/TDM local lightwave network with arbitrary transceiver tuning latencies

Abstract: A passive-star-based, broadcast-and-select, local lightwave network which can support a limited number of wavelength-division multiplexed (WDM) channels, but serve a much larger number of nodes, is considered. Each node is equipped with one tunable transmitter and one fixed receiver, and each WDM channel is operated in a time-division multiplexed (TDM) fashion for carrying packet traffic. Bandwidth is allocated to the node pairs when traffic flow between them is nonuniform, while also accommodating transceiver tuning latency. Our approach exploits well-known results from scheduling theory to create efficient transmission schedules. Multiprocessor task scheduling heuristics that can be applied to load balancing in a multichannel network is also examined.

The impact of stigma on medication adherence among HIV-positive adolescent and young adult females and the moderating effects of coping and satisfaction with health care.

Abstract: To explore whether HIV stigma negatively impacts adherence to antiviral medications in HIV-infected adolescent women, moderational analysis was conducted and factors identified that could alter said relationship. Study participants were 178 adolescent females age 15–24, enrolled between 2003–2005, from 5 different cities and 60 provided adherence information. Findings reported by this cohort of 60 adolescent women included: medication adherence, 64.3% reporting adherence at baseline and 45.0% at 12 months; HIV stigma score of 57.60 (standard deviation [SD], 11.83; range, 25–86). HIV stigma was not found to be a significant predictor when binary logit regression was run with medication adherence at 1 year. Using moderational analysis, factors that could moderate stigma's effect on medication adherence was still pursued and identified the following to be significant at 12 months: health care satisfaction (B=−0.020, standard error [SE]=0.010, p<.05); and Coping (proactive coping strategies [B=0.012,...

People Out of Place : Globalization, Human Rights and the Citizenship Gap

Abstract: Part 1: Framework 1 Globalization and the Citizenship Gap Alison Brysk and Gershon Shafir 2 Citizenship and Human Rights In An Era of Globalization Gershon Shafir Part 2: Producing Citizenship 3 Constituting Political Community Ronnie Lipschutz 4 Latitudes of Citizenship Aihwa Ong Part 3: Constructing Rights 5 Agency on a Global Scale: Rules, rights and the European Union David Jacobson and Galya Benarieh Ruffer 6 International Law and Citizenship: Mandated membership, diluted identity Peter Spiro Part 4: Globalizing the Citizenship Gap 7 Deflated Citizenship: Labor rights in a global era Gay W Seidman 8 The Globalization of Social Reproduction: Women migrants Kristen Hill Maher 9 Children Across Borders: Patrimony, property or persons? Alison Brysk Part 5: Reconstructing Citizenship 10 Citizenship and Globalism: Markets, empire and terrorism Richard Falk 11 The Repositioning of Citizenship Saskia Sassen 12 Globalizing Citizenship? Alison Brysk and Gershon Shafir

Robustness of collaborative recommendation based on association rule mining

Abstract: Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than k-nn. Model abstraction can inhibit certain aspects of an attack, providing an algorithmic approach to minimizing attack effectiveness. In this paper, we examine the robustness of a recommendation algorithm based on the data mining technique of association rule mining. Our results show that the Apriori algorithm offers large improvement in stability and robustness compared to k-nearest neighbor and other model-based techniques we have studied. Furthermore, our results show that Apriori can achieve comparable recommendation accuracy to k-nn.