Applied Randomized Algorithms for Efficient Genomic Analysis

The scope and scale of biological data continues to grow at an exponential clip, driven by advances in genetic sequencing, annotation and widespread adoption of surveillance efforts. For instance, the Sequence Read Archive (SRA) now contains more than 25 petabases of public data, while RefSeq, a collection of reference genomes, recently surpassed 100,000 complete genomes. In the process, it has outgrown the practical reach of many traditional algorithmic approaches in both time and space. Motivated by this extreme scale, this thesis details efficient methods for clustering and summarizing large collections of sequence data. While our primary area of interest is biological sequences, these approaches largely apply to sequence collections of any type, including natural language, software source code, and graph structured data. We applied recent advances in randomized algorithms to practical problems. We used MinHash and HyperLogLog, both examples of Locality- Sensitive Hashing, as well as coresets, which are approximate representations for finite sum problems, to build methods capable of scaling to billions of items. Ultimately, these are all derived from variations on sampling. We combined these advances with hardware-based optimizations and incorporated into free and open-source software libraries (sketch, frp, lib- simdsampling) and practical software tools built on these libraries (Dashing, Minicore, Dashing 2), empowering users to interact practically with colossal datasets on commodity hardware

Integrated modeling and design of lightweight, active mirrors for launch survival and on-orbit performance

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 241-256).Lightweight, active mirrors are an enabling technology for large aperture, space-based optical systems. These mirrors have the potential to improve the optical resolution and sensitivity beyond what is currently possible. However, as with all technology development programs, there are remaining issues to be solved before such mirrors can be used in operational systems. As of yet, no efforts have been made to explore the design space or optimize the design of lightweight mirrors across operational environments and constraints. The extremely harsh launch environment is of particular concern because launch survival constraints could dictate aspects of the mirror design. Additionally, on-orbit optical performance, in terms of high spatial frequency wavefront error and low spatial frequency correctability, are extremely important aspects of mirror design. Due to the lack of heritage systems, the best designs for lightweight, active mirrors are not immediately apparent. Therefore, an integrated modeling methodology for technology development programs is developed. This framework uses model-based design and evolutionary models to guide the technology development program. This methodology is applied to the lightweight, active mirror systems of interest. The mirrors are modeled and analyzed in two distinct environments: on-orbit and during launch. The on-orbit model and analysis are presented, as well as the designs with the best optical performance, which tend to have many ribs and actuators. Additionally, a dynamic state-space model of the launch environment is developed. The designs that are most likely to survive launch have few ribs and actuators, directly in conflict with the best on-orbit designs. Launch load alleviation techniques, including techniques making use of the existing embedded actuators, are also implemented to increase the probability of launch survival. Finally, a fully integrated trade space analysis of designs is shown, along with families of designs that perform well with respect to different mission objectives. The integrated modeling approach allows for the seamless combination of the two analysis, as well as a way in which to determine the best performing designs. By using this approach, the model can be updated to include any new insights and to reflect the current state of the technology, making it useful throughout the life cycle of the program.by Lucy E. Cohan.Ph.D

Teaching and Learning of Fluid Mechanics

This book contains research on the pedagogical aspects of fluid mechanics and includes case studies, lesson plans, articles on historical aspects of fluid mechanics, and novel and interesting experiments and theoretical calculations that convey complex ideas in creative ways. The current volume showcases the teaching practices of fluid dynamicists from different disciplines, ranging from mathematics, physics, mechanical engineering, and environmental engineering to chemical engineering. The suitability of these articles ranges from early undergraduate to graduate level courses and can be read by faculty and students alike. We hope this collection will encourage cross-disciplinary pedagogical practices and give students a glimpse of the wide range of applications of fluid dynamics

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described