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Lancaster, PA, United States

Franklin & Marshall College is a four-year private co-educational residential national liberal arts college in the Northwest Corridor neighborhood of Lancaster, Pennsylvania, United States. It employs 175 full-time faculty members and has a student body of approximately 2,324 full-time students Wikipedia.


Ismat Z.,Franklin And Marshall College
Journal of Structural Geology | Year: 2013

Recent work from portions of the Sevier fold-thrust belt that have deformed primarily within the elastico-frictional regime, demonstrates that cataclastic flow can be subdivided into two types: matrix- and block supported. The two types may operate simultaneously within the same deforming material. However, their activity can vary spatially, temporally and across scales. Although block-supported cataclastic flow is a critical process in upper crustal deformation, it continues to be largely ignored and/or misunderstood, primarily because established concepts and definitions for cataclastic flow are chiefly based on matrix-supported cataclastic flow. Here, block-supported cataclastic flow is examined to better understand cataclastic flow in general and to explore its relationship with matrix-supported cataclastic flow. © 2013 Elsevier Ltd. Source


Lommen A.N.,Franklin And Marshall College
Reports on Progress in Physics | Year: 2015

We describe the history, methods, tools, and challenges of using pulsars to detect gravitational waves. Pulsars act as celestial clocks detecting gravitational perturbations in space-time at wavelengths of light-years. The field is poised to make its first detection of nanohertz gravitational waves in the next 10 years. Controversies remain over how far we can reduce the noise in the pulsars, how many pulsars should be in the array, what kind of source we will detect first, and how we can best accommodate our large bandwidth systems. We conclude by considering the important question of how to plan for a post-detection era, beyond the first detection of gravitational waves. © 2015 IOP Publishing Ltd. Source


Drawing on participant-observation in Nicaraguan dengue prevention campaigns and a series of semistructured interviews with Nicaraguan health ministry personnel, this article shows how community health workers (CHWs) balanced two kinds of "medical citizenship." In some situations, CHWs acted as professional monitors and models of hygienic behavior. At other times, CHWs acted as compassionate advocates for their poor neighbors. In 2008, Nicaragua's Sandinista government moved to end a long-standing policy of paying CHWs, recasting them as citizen-volunteers in a "popular struggle" against dengue. Although CHWs approved of the revival of grassroots advocacy, they were hostile to the elimination of compensation. Framing this ambivalence as part of CHWs' desire to serve as "brokers" between the poor and the state, I suggest that attention to medical citizenship provides insight into the sometimes contradictory ways in which CHWs engage the participatory health policies now taking hold in Latin America and elsewhere. © 2013 by the American Anthropological Association. Source


Grant
Agency: NSF | Branch: Continuing grant | Program: | Phase: ROBUST INTELLIGENCE | Award Amount: 89.89K | Year: 2016

As humans and other animals navigate the world they demonstrate remarkable flexibility in encountering unfamiliar systems, spaces and phenomena, learning to make predictions about how they will behave, and making good decisions based on those predictions. Crucial to this ability is the fact that one does not need to make perfectly accurate or fully detailed predictions to make good decisions. Though, due to our natural limitations, our predictions about the future are necessarily flawed, they are nevertheless sufficiently useful to make reasonable decisions. For artificial agents, in contrast, imperfect predictions often lead to catastrophic failures in decision making. Many existing approaches fundamentally assume that the agent will eventually learn to make perfect predictions and make perfect decisions, which is unreasonable in sufficiently rich, complex environments. This work considers the problem of developing artificial agents that are more aware of and more robust to their own limitations. Agents that can more robustly and flexibly learn from experience in truly complex environments have the potential to impact nearly any application in which decisions are made over time, for instance autonomous robots/vehicles, personal assistants, and medical/legal decision support. Furthermore, as the project will be undertaken at an undergraduate-only liberal arts college, undergraduate researchers will play an integral role in the work. The PI will also build on the strength of the liberal arts setting to enhance instruction of key discipline-specific research and writing skills throughout the Computer Science curriculum. Explicit development of these skills will not only improve students preparation for a wide variety of career paths (including basic research) but is also aligned with best practices for broadening participation in the discipline.

This project studies model-based reinforcement learning (MBRL) under the assumption that the agent has fundamental limitations that prevent it from learning a perfect model or from producing optimal plans. The central hypothesis is that in this context the MBRL problem cannot be decomposed into separate model-learning and planning problems, each treating the other as an idealized black box. Rather the optimization process for each component must be aware of its role in the overall architecture and of the limitations of its partner. One key aim of the work is to derive novel measures of model quality that are more tightly related to the true objective of control performance than standard measures of one-step prediction accuracy adapted from supervised learning settings. Another is to investigate how model learning objectives/algorithms can be adapted to account for the limitations of the specific planner that will use the model. Further, control algorithms will be investigated that can make effective use of models of non-homogeneous quality by mediating between model-based and model-free knowledge. The ultimate goal is to integrate these principles into novel MBRL agents that are significantly more robust to limitations in the model class and/or planner and are able to succeed in environments that are too complex and high-dimensional to be modeled or solved exactly.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: Physiolg Mechansms&Biomechancs | Award Amount: 273.04K | Year: 2016

Squids and cuttlefishes are impressive swimmers, having the ability to hover, change direction rapidly, and even swim forward and backward with ease. The key to their locomotive prowess is coordination among their pulsed jet, flapping fins, and flexible arms, but little is presently known about how these units work together throughout these animals lives as they encounter different physical environments, change developmentally, and experience dissimilar ecosystems. This project focuses on understanding how the jet, fins, and arms operate in concert to produce the necessary forces for exceptional turning, both in terms of muscle capabilities and hydrodynamics, in squid and cuttlefish of different developmental stages (hatchlings to adults). This work will involve cutting edge 3D flow visualization approaches, high-speed video analysis, and advanced mathematical tools that highlight the essential components of high-performance turns. This project promises to (1) advance our understanding of how highly maneuverable marine animals navigate through their complex habitats and (2) reveal key performance characteristics, structures, and behaviors that can be integrated potentially into the design of mechanical bio-inspired systems, such as autonomous underwater vehicles, to improve their turning/docking capabilities. This project incorporates a number of outreach projects, including demonstrations in local schools, participation in robotics competitions, development of web-based tutorials and summer camps, and presentations at aquariums and museums.

Maneuvering in the aquatic environment is a significant component of routine swimming, with proficient maneuvering being essential for predator avoidance, prey capture, and navigation. Despite its importance, understanding of the biomechanics of maneuvering behaviors is limited. An investigation of maneuvering performance in three morphologically distinct species of cephalopods is proposed here. The investigation explores three broad questions: (1) how are the fins, arms, and funnel-jet complex used in concert to maximize turning performance in adult cephalopods; (2) do the relative importance of turning rate and turning radius change over ontogeny and are fewer turning modes observed in young cephalopods; and (3) do fin, arm, and funnel musculoskeletal mechanics change over ontogeny and are such changes associated with differences in maneuvering? These questions will be addressed by collecting measurements of 3D high-speed kinematics and 2D/3D hydrodynamics of wake flows; performing mathematical analyses to quantitatively identify and categorize turning patterns; and measuring both the dynamic passive and active length-force relationship and maximum shortening velocity of muscle fibers that drive the movements used during turning and jet vectoring. The proposed work will: (1) provide data on how an ecologically important marine animal coordinates its novel dual-mode system (jet and fins) and arms to achieve high turning performance, (2) highlight the essential kinematic and hydrodynamic elements of turns, (3) offer insights into how maneuvering capabilities change over a broad ontogenetic range, and (4) provide novel data on the muscle properties of muscular hydrostatic organs and their role in turning.

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