Latent demographic profile estimation in hard-to-reach groups

The sampling frame in most social science surveys excludes members of certain groups, known as hard-to-reach groups. These groups, or subpopulations, may be difficult to access (the homeless, e.g.), camouflaged by stigma (individuals with HIV/AIDS), or both (commercial sex workers). Even basic demographic information about these groups is typically unknown, especially in many developing nations. We present statistical models which leverage social network structure to estimate demographic characteristics of these subpopulations using Aggregated relational data (ARD), or questions of the form "How many X's do you know?" Unlike other network-based techniques for reaching these groups, ARD require no special sampling strategy and are easily incorporated into standard surveys. ARD also do not require respondents to reveal their own group membership. We propose a Bayesian hierarchical model for estimating the demographic characteristics of hard-to-reach groups, or latent demographic profiles, using ARD. We propose two estimation techniques. First, we propose a Markov-chain Monte Carlo algorithm for existing data or cases where the full posterior distribution is of interest. For cases when new data can be collected, we propose guidelines and, based on these guidelines, propose a simple estimate motivated by a missing data approach. Using data from McCarty et al. [Human Organization 60 (2001) 28-39], we estimate the age and gender profiles of six hard-to-reach groups, such as individuals who have HIV, women who were raped, and homeless persons. We also evaluate our simple estimates using simulation studies.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS569 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Active influence in dynamical models of structural balance in social networks

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of "friendliness levels" in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.Comment: 7 pages, 3 figures, to appear in Europhysics Letters (http://www.epletters.net

The action of certain substituted phenols on marine eggs in relation to their dissociation

It has been shown by Clowes and Krahl (1, 2) that various substituted phenols as well as dinitrophenol increase the respiratory rate of marine eggs. Also, the highly interesting reversible block to cleavage, which they found to occur at the maximum of respiratory stimulation, is likewise exhibited. The different substances (nitro- and halo-phenols and cresols in particular) used were found to be active in different concentrations, and some attempt is made to relate the activity to molecular structure. The degree of dissociation of the phenolic OH is taken to be of no significance in their experiments. There has been some controversy concerning this question. Field, Martin and Field (3, 4) showed that in yeast the amount of respiratory stimulation by 2,4-dinitrophenol and by 4,6-dinitrocresol depends upon the concentration of the undissociated form present, similar calculated concentrations of undissociated DNP giving at different pH's the same stimulation. Citing their own experiments and those of Ehrenfest and Ronzoni (5) on yeast, De Meio and Barron (6), on the other hand, disagree with this conclusion

Distributed Model Predictive Consensus via the Alternating Direction Method of Multipliers

We propose a distributed optimization method for solving a distributed model predictive consensus problem. The goal is to design a distributed controller for a network of dynamical systems to optimize a coupled objective function while respecting state and input constraints. The distributed optimization method is an augmented Lagrangian method called the Alternating Direction Method of Multipliers (ADMM), which was introduced in the 1970s but has seen a recent resurgence in the context of dramatic increases in computing power and the development of widely available distributed computing platforms. The method is applied to position and velocity consensus in a network of double integrators. We find that a few tens of ADMM iterations yield closed-loop performance near what is achieved by solving the optimization problem centrally. Furthermore, the use of recent code generation techniques for solving local subproblems yields fast overall computation times.Comment: 7 pages, 5 figures, 50th Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 201

Restoring Trust Relationships within Collaborative Digital Preservation Federations

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PresentationsDate: 2009-05-19 01:00 PM – 02:30 PMThe authors extend their process for creating and establishing trust relationships to include steps for restoring trust relationships after catastrophic events. Part of this model will include best practices for business continuity relationships and will integrate trust models from Holland and Lockett (1998) and Ring and Van de Ven (1994) and how they can be applied to a process for trust restoration after periods of disaster or critical data loss. These models provide key frameworks for understanding how trust can be utilized for collaborative start points as well as for collaborative recovery points from physical natural disaster or critical data loss