Haitians in Miami: An Assessment of Their Background and Potential (Dialogue #12)

EXECUTIVE SUMMARY Our research on Haitians in Miami reveals the common stereotypes to be wrong in virtually every respect. Miami Haitians are not a significant drain on community resources. They did not come to the U.S. anticipating benefits from the welfare system. They are not uneducated nor are they unskilled. To the contrary, Miami Haitians have a tremendous potential for productively contributing to U.S. society. They are well educated by Haitian standards and many come with readily employable skills. Their motivations for leaving Haiti are inseparably both political and economic. They possess a sound work ethic and are striving to improve themselves. Economic problems are severe, yet they confront and surmount them with virtually no help from the state welfare system. They rely largely upon opportunities and resources within Miami's own Haitian community. Yet, they do not isolate themselves from the large community around them. They work with, buy from, and live in the same neighborhoods as Cubans, Anglos, and American Blacks. In spite of the many personal difficulties they have encountered since arriving in the U.S., they maintain a positive view both of themselves and their experiences in U.S. society. If given sufficient opportunities, they are likely to adapt quickly and succeed economically. These findings stem from a recently completed survey of 129 Haitians enrolled in English for Speakers of Other Languages (ESOL) classes in Miami administered by the Haitian Adult Development Education Program (HADEP) of the Phelps Stokes Fund. The U.S. Department of Education funded the project to provide instruction in English communication and literacy skills, acculturation support and vocational training. The classes were free and open without restrictions to all Haitians. The Haitians neither paid nor received money to attend the classes. The classes were offered both during the day and evening and drew from all levels of the Haitian population in Miami. The survey was administered in June and July of this year and consisted of 146 questions in Creole on a broad range of subjects from background and experiences in Haiti to migration and employment history and perceptions of U.S. society.

Section I: Institutional setting; Background Information about the School

Document detailing the Institutional Setting of the FLorida International University College of Medicine. Part of the Medical Education Database for Preliminary Accreditation, 2006-2007.

Background traffic modeling for large-scale network simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.

Aerial view of Shark River Slough with SRS-2 in the background

http://digitalcommons.fiu.edu/fce_lter_photos/1271/thumbnail.jpg

Background Traffic Modeling for Large-Scale Network Simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.