Analysis of Deterministic and Stochastic HIV Models

In this research paper, I apply the Susceptible-Infected-Virus (SIV) model to the Human Immunodeficiency Virus (HIV). The SIV model is a compartmental model describing within-host dynamics of viral infections; I analyze it in both its deterministic (in which constants are assumed to be known exactly) and stochastic (in which the death rate of the healthy cells is represented by a random variable) forms. First, I give analytical solutions to two simplified versions of the deterministic model. Next, I apply numerical methods to the full deterministic and stochastic systems. The results give an illustrative picture of HIV in-host population dynamics in the absence of treatment. They also demonstrate how randomness can impact the progression of the disease

1969 Clinic Yearbook

The Clinic is the yearbook of the Sidney Kimmel Medical College (formerly Jefferson Medical College) at Thomas Jefferson University

Reinforcing the Safety Net: A Collaborative Survey with the Massachusetts Nonprofit Network

The more than 35,000 nonprofit organizations in Massachusetts employ 20% of the state’s workforce and serve as a vital part of the social safety net. Many of these organizations face challenges concerning fiscal sustainability. Funding often covers current services with little surplus to address organizational capacity issues. Successful public-nonprofit partnerships are key to building a resilient nonprofit sector. This study contributes to the nonprofit sector’s knowledge of how best to engage with policymakers at the state and local level

The Retreat of Influence: Exploring the Decline of Nonprofit Advocacy and Public Engagement

Key findings:A significantly lower proportion of nonprofits report advocating or lobbying compared to 20 years ago.Mission plays the largest role in determining nonprofit advocacy and lobbying.Today, significantly fewer nonprofits know advocacy activities they are legally allowed to do compared to 20 years ago.Although a majority of nonprofits have a diversity, equity, and inclusion (DEI) statement, only 36% of them engage in policy activities to create more equitable systems.Nonprofits that belong to collaborative groups advocate at higher rates than those that are not members.Only 13% of nonprofits conduct nonpartisan activities to help people vote

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

The use of linear algebra routines is fundamental to many areas of computational science, yet their implementation in software still forms the main computational bottleneck in many widely used algorithms. In machine learning and computational statistics, for example, the use of Gaussian distributions is ubiquitous, and routines for calculating the Cholesky decomposition, matrix inverse and matrix determinant must often be called many thousands of times for common algorithms, such as Markov chain Monte Carlo. These linear algebra routines consume most of the total computational time of a wide range of statistical methods, and any improvements in this area will therefore greatly increase the overall efficiency of algorithms used in many scientific application areas. The importance of linear algebra algorithms is clear from the substantial effort that has been invested over the last 25 years in producing low-level software libraries such as LAPACK, which generally optimise these linear algebra routines by breaking up a large problem into smaller problems that may be computed independently. The performance of such libraries is however strongly dependent on the specific hardware available. LAPACK was originally developed for single core processors with a memory hierarchy, whereas modern day computers often consist of mixed architectures, with large numbers of parallel cores and graphics processing units (GPU) being used alongside traditional CPUs. The challenge lies in making optimal use of these different types of computing units, which generally have very different processor speeds and types of memory. In this thesis we develop novel low-level algorithms that may be generally employed in blocked linear algebra routines, which automatically optimise themselves to take full advantage of the variety of heterogeneous architectures that may be available. We present a comparison of our methods with MAGMA, the state of the art open source implementation of LAPACK designed specifically for hybrid architectures, and demonstrate up to 400% increase in speed that may be obtained using our novel algorithms, specifically when running commonly used Cholesky matrix decomposition, matrix inverse and matrix determinant routines

Sidekick agents for sequential planning problems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 127-131).Effective Al sidekicks must solve the interlinked problems of understanding what their human collaborator's intentions are and planning actions to support them. This thesis explores a range of approximate but tractable approaches to planning for AI sidekicks based on decision-theoretic methods that reason about how the sidekick's actions will effect their beliefs about unobservable states of the world, including their collaborator's intentions. In doing so we extend an existing body of work on decision-theoretic models of assistance to support information gathering and communication actions. We also apply Monte Carlo tree search methods for partially observable domains to the problem and introduce an ensemble-based parallelization strategy. These planning techniques are demonstrated across a range of video game domains.by Owen Macindoe.Ph.D