Highly intensive data dissemination in complex networks

This paper presents a study on data dissemination in unstructured Peer-to-Peer (P2P) network overlays. The absence of a structure in unstructured overlays eases the network management, at the cost of non-optimal mechanisms to spread messages in the network. Thus, dissemination schemes must be employed that allow covering a large portion of the network with a high probability (e.g.~gossip based approaches). We identify principal metrics, provide a theoretical model and perform the assessment evaluation using a high performance simulator that is based on a parallel and distributed architecture. A main point of this study is that our simulation model considers implementation technical details, such as the use of caching and Time To Live (TTL) in message dissemination, that are usually neglected in simulations, due to the additional overhead they cause. Outcomes confirm that these technical details have an important influence on the performance of dissemination schemes and that the studied schemes are quite effective to spread information in P2P overlay networks, whatever their topology. Moreover, the practical usage of such dissemination mechanisms requires a fine tuning of many parameters, the choice between different network topologies and the assessment of behaviors such as free riding. All this can be done only using efficient simulation tools to support both the network design phase and, in some cases, at runtime

LUNES: Agent-based Simulation of P2P Systems (Extended Version)

We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which allows to simulate complex networks composed of a high number of nodes. LUNES is modular, since it splits the three phases of network topology creation, protocol simulation and performance evaluation. This permits to easily integrate external software tools into the main software architecture. The simulation of the interaction protocols among network nodes is performed via a simulation middleware that supports both the sequential and the parallel/distributed simulation approaches. In the latter case, a specific mechanism for the communication overhead-reduction is used; this guarantees high levels of performance and scalability. To demonstrate the efficiency of LUNES, we test the simulator with gossip protocols executed on top of networks (representing peer-to-peer overlays), generated with different topologies. Results demonstrate the effectiveness of the proposed approach.Comment: Proceedings of the International Workshop on Modeling and Simulation of Peer-to-Peer Architectures and Systems (MOSPAS 2011). As part of the 2011 International Conference on High Performance Computing and Simulation (HPCS 2011

Evidence-based implementation practices applied to the intensive treatment of eating disorders: Summary of research and illustration of principles using a case example

Implementation of evidence‐based practices (EBPs) in intensive treatment settings poses a major challenge in the field of psychology. This is particularly true for eating disorder (ED) treatment, where multidisciplinary care is provided to a severe and complex patient population; almost no data exist concerning best practices in these settings. We summarize the research on EBP implementation science organized by existing frameworks and illustrate how these practices may be applied using a case example. We describe the recent successful implementation of EBPs in a community‐based intensive ED treatment network, which recently adapted and implemented transdiagnostic, empirically supported treatment for emotional disorders across its system of residential and day‐hospital programs. The research summary, implementation frameworks, and case example may inform future efforts to implement evidence‐based practice in intensive treatment settings.Published versio

Efficient Opinion Sharing in Large Decentralised Teams

In this paper we present an approach for improving the accuracy of shared opinions in a large decentralised team. Specifically, our solution optimises the opinion sharing process in order to help the majority of agents to form the correct opinion about a state of a common subject of interest, given only few agents with noisy sensors in the large team. We build on existing research that has examined models of this opinion sharing problem and shown the existence of optimal parameters where incorrect opinions are filtered out during the sharing process. In order to exploit this collective behaviour in complex networks, we present a new decentralised algorithm that allows each agent to gradually regulate the importance of its neighbours' opinions (their social influence). This leads the system to the optimised state in which agents are most likely to filter incorrect opinions, and form a correct opinion regarding the subject of interest. Crucially, our algorithm is the first that does not introduce additional communication over the opinion sharing itself. Using it 80-90% of the agents form the correct opinion, in contrast to 60-75% with the existing message-passing algorithm DACOR proposed for this setting. Moreover, our solution is adaptive to the network topology and scales to thousands of agents. Finally, the use of our algorithm allows agents to significantly improve their accuracy even when deployed by only half of the team

Final Report from the Models for Change Evaluation

Note: This evaluation is accompanied by an evaluation of the National Campaign for this initiative as well as introduction to the evaluation effort by MacArthur's President, Julia Stasch, and a response to the evaluation from the program team. Access these related materials here (https://www.macfound.org/press/grantee-publications/evaluation-models-change-initiative).Models for Change is an initiative of The John D. and Catherine T. MacArthur Foundationto accelerate juvenile justice reforms and promote fairer, more effective, and more developmentally appropriate juvenile justice systems throughout the United States. Between 2004 and 2014, the Foundation invested more than $121 million in the initiative, intending to create sustainable and replicable models of systems reform.In June 2013, the Foundation partnered with Mathematica Policy Research and the University of Maryland to design and conduct a retrospective evaluation of Models for Change. The evaluation focused on the core state strategy, the action network strategy, and the national context in which Models for Change played out. This report is a digest and synthesis of several technical reports prepared as part of the evaluation

Blueprint for the Dissemination of Evidence-Based Practices in Health Care

Proposes strategies for better dissemination of best practices through quality improvement campaigns, including campaigns aligned with adopting organizations' goals, practical implementation tools and guides, and networks to foster learning opportunities

Opinion Dynamics and Communication Networks

This paper examines the interplay of opinion exchange dynamics and communication network formation. An opinion formation procedure is introduced which is based on an abstract representation of opinions as $k$--dimensional bit--strings. Individuals interact if the difference in the opinion strings is below a defined similarity threshold $d_I$. Depending on $d_I$, different behaviour of the population is observed: low values result in a state of highly fragmented opinions and higher values yield consensus. The first contribution of this research is to identify the values of parameters $d_I$ and $k$, such that the transition between fragmented opinions and homogeneity takes place. Then, we look at this transition from two perspectives: first by studying the group size distribution and second by analysing the communication network that is formed by the interactions that take place during the simulation. The emerging networks are classified by statistical means and we find that non--trivial social structures emerge from simple rules for individual communication. Generating networks allows to compare model outcomes with real--world communication patterns.Comment: 14 pages 6 figure