Pricing in Social Networks with Negative Externalities

We study the problems of pricing an indivisible product to consumers who are embedded in a given social network. The goal is to maximize the revenue of the seller. We assume impatient consumers who buy the product as soon as the seller posts a price not greater than their values of the product. The product's value for a consumer is determined by two factors: a fixed consumer-specified intrinsic value and a variable externality that is exerted from the consumer's neighbors in a linear way. We study the scenario of negative externalities, which captures many interesting situations, but is much less understood in comparison with its positive externality counterpart. We assume complete information about the network, consumers' intrinsic values, and the negative externalities. The maximum revenue is in general achieved by iterative pricing, which offers impatient consumers a sequence of prices over time. We prove that it is NP-hard to find an optimal iterative pricing, even for unweighted tree networks with uniform intrinsic values. Complementary to the hardness result, we design a 2-approximation algorithm for finding iterative pricing in general weighted networks with (possibly) nonuniform intrinsic values. We show that, as an approximation to optimal iterative pricing, single pricing can work rather well for many interesting cases, but theoretically it can behave arbitrarily bad

Ground-State Dynamical Correlation Functions: An Approach from Density Matrix Renormalization Group Method

A numerical approach to ground-state dynamical correlation functions from Density Matrix Renormalization Group (DMRG) is developed. Using sum rules, moments of a dynamic correlation function can be calculated with DMRG, and with the moments the dynamic correlation function can be obtained by the maximum entropy method. We apply this method to one-dimensional spinless fermion system, which can be converted to the spin 1/2 Heisenberg model in a special case. The dynamical density-density correlation function is obtained.Comment: 11 pages, latex, 4 figure

Health information technologies in geriatrics and gerontology: a mixed systematic review

Objective: To review, categorize, and synthesize findings from the literature about the application of health information technologies in geriatrics and gerontology (GGHIT). Materials and Methods: This mixed-method systematic review is based on a comprehensive search of Medline, Embase, PsychInfo and ABI/Inform Global. Study selection and coding were performed independently by two researchers and were followed by a narrative synthesis. To move beyond a simple description of the technologies, we employed and adapted the diffusion of innovation theory (DOI). Results: 112 papers were included. Analysis revealed five main types of GGHIT: (1) telecare technologies (representing half of the studies); (2) electronic health records; (3) decision support systems; (4) web-based packages for patients and/or family caregivers; and (5) assistive information technologies. On aggregate, the most consistent finding proves to be the positive outcomes of GGHIT in terms of clinical processes. Although less frequently studied, positive impacts were found on patients’ health, productivity, efficiency and costs, clinicians’ satisfaction, patients’ satisfaction and patients’ empowerment. Discussion: Further efforts should focus on improving the characteristics of such technologies in terms of compatibility and simplicity. Implementation strategies also should be improved as trialability and observability are insufficient. Conclusions: Our results will help organizations in making decisions regarding the choice, planning and diffusion of GGHIT implemented for the care of older adults

Challenges of data provenance for cloud forensic investigations

Cloud computing has gained popularity due to its efficiency, robustness and cost effectiveness. Carrying out digital forensic investigations in the cloud is currently a relevant and open issue. The root of this issue is the fact that servers cannot be physically accessed, coupled with the dynamic and distributed nature of cloud computing with regards to data processing and storage. This renders traditional methods of evidence collection impractical. The use of provenance data in cloud forensics is critical as it provides forensic investigators with data history in terms of people, entities and activities involved in producing related data objects. Therefore, cloud forensics requires effective provenance collection mechanisms. This paper provides an overview of current provenance challenges in cloud computing and identifies limitations of current provenance collection mechanisms. Recommendations for additional research in digital provenance for cloud forensics are also presented