A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves

Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care

Early word learning through communicative inference

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-122).How do children learn their first words? Do they do it by gradually accumulating information about the co-occurrence of words and their referents over time, or are words learned via quick social inferences linking what speakers are looking at, pointing to, and talking about? Both of these conceptions of early word learning are supported by empirical data. This thesis presents a computational and theoretical framework for unifying these two different ideas by suggesting that early word learning can best be described as a process of joint inferences about speakers' referential intentions and the meanings of words. Chapter 1 describes previous empirical and computational research on "statistical learning"--the ability of learners to use distributional patterns in their language input to learn about the elements and structure of language-and argues that capturing this abifity requires models of learning that describe inferences over structured representations, not just simple statistics. Chapter 2 argues that social signals of speakers' intentions, even eye-gaze and pointing, are at best noisy markers of reference and that in order to take advantage of these signals fully, learners must integrate information across time. Chapter 3 describes the kinds of inferences that learners can make by assuming that speakers are informative with respect to their intended meaning, introducing and testing a formalization of how Grice's pragmatic maxims can be used for word learning. Chapter 4 presents a model of cross-situational intentional word learning that both learns words and infers speakers' referential intentions from labeled corpus data.by Michael C. Frank.Ph.D

Supporting strategic design of workplace environments with case-based reasoning

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Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms