2,930 research outputs found

    Mapping Critical Practice In A Transdisciplinary Urban Studio

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    Architecture and Planning exist to make positive changes to our environment. Future practitioners in these disciplines will be responsible for how our cities develop and are managed - they will be required to exercise their professional judgement in complex and unpredictable contexts. There is increasing interest in transdisciplinary urbanism, but implementation in academic contexts has to date been relatively limited. This thesis aims to build on these examples, through a detailed account of one academic design studio which operates across architecture and urban planning; in doing so it aims to make the case for transdisciplinary, problem and place-based studio teaching. The study considers how a transdisciplinary studio environment supported students to develop a critical approach to practice through collaborative discourse. It looked at studio methods/practices; what it means to practice ‘critically’ in the context of design; and the role ‘going public’ by sharing ideas in public fora might play in developing critical positions. The study was undertaken in collaboration with nine students, a single cohort undertaking the final year of a hybrid master’s qualification in Architecture with Urban Planning. It adopts socio-material and spatial approaches to follow how the studio environment and the students’ emerging interdisciplinary identities shaped both their individual and their shared work. It mapped how their approach to their practice evolved through observations, interviews, and informal conversations, and through their drawings, models and journals. In carrying out these observations, and their analysis, I have returned to drawing methods common in architecture. This allowed me to explore and record aspects of studio practice which might otherwise be missed and revealed the importance of visual and spatial thinking to my own practice. Observations revealed how material spaces, tools and artefacts acted to structure social relations in the studio, and how these relations shaped individual approaches to critical practice

    Backpropagation Beyond the Gradient

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    Automatic differentiation is a key enabler of deep learning: previously, practitioners were limited to models for which they could manually compute derivatives. Now, they can create sophisticated models with almost no restrictions and train them using first-order, i. e. gradient, information. Popular libraries like PyTorch and TensorFlow compute this gradient efficiently, automatically, and conveniently with a single line of code. Under the hood, reverse-mode automatic differentiation, or gradient backpropagation, powers the gradient computation in these libraries. Their entire design centers around gradient backpropagation. These frameworks are specialized around one specific task—computing the average gradient in a mini-batch. This specialization often complicates the extraction of other information like higher-order statistical moments of the gradient, or higher-order derivatives like the Hessian. It limits practitioners and researchers to methods that rely on the gradient. Arguably, this hampers the field from exploring the potential of higher-order information and there is evidence that focusing solely on the gradient has not lead to significant recent advances in deep learning optimization. To advance algorithmic research and inspire novel ideas, information beyond the batch-averaged gradient must be made available at the same level of computational efficiency, automation, and convenience. This thesis presents approaches to simplify experimentation with rich information beyond the gradient by making it more readily accessible. We present an implementation of these ideas as an extension to the backpropagation procedure in PyTorch. Using this newly accessible information, we demonstrate possible use cases by (i) showing how it can inform our understanding of neural network training by building a diagnostic tool, and (ii) enabling novel methods to efficiently compute and approximate curvature information. First, we extend gradient backpropagation for sequential feedforward models to Hessian backpropagation which enables computing approximate per-layer curvature. This perspective unifies recently proposed block- diagonal curvature approximations. Like gradient backpropagation, the computation of these second-order derivatives is modular, and therefore simple to automate and extend to new operations. Based on the insight that rich information beyond the gradient can be computed efficiently and at the same time, we extend the backpropagation in PyTorch with the BackPACK library. It provides efficient and convenient access to statistical moments of the gradient and approximate curvature information, often at a small overhead compared to computing just the gradient. Next, we showcase the utility of such information to better understand neural network training. We build the Cockpit library that visualizes what is happening inside the model during training through various instruments that rely on BackPACK’s statistics. We show how Cockpit provides a meaningful statistical summary report to the deep learning engineer to identify bugs in their machine learning pipeline, guide hyperparameter tuning, and study deep learning phenomena. Finally, we use BackPACK’s extended automatic differentiation functionality to develop ViViT, an approach to efficiently compute curvature information, in particular curvature noise. It uses the low-rank structure of the generalized Gauss-Newton approximation to the Hessian and addresses shortcomings in existing curvature approximations. Through monitoring curvature noise, we demonstrate how ViViT’s information helps in understanding challenges to make second-order optimization methods work in practice. This work develops new tools to experiment more easily with higher-order information in complex deep learning models. These tools have impacted works on Bayesian applications with Laplace approximations, out-of-distribution generalization, differential privacy, and the design of automatic differentia- tion systems. They constitute one important step towards developing and establishing more efficient deep learning algorithms

    Microcredentials to support PBL

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    Efficient Model Checking: The Power of Randomness

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    2023-2024 Graduate School Catalog

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    You and your peers represent more than 67 countries and your shared scholarship spans 140 programs - from business administration and biomedical engineering to history, horticulture, musical performance, marine science, and more. Your ideas and interests will inform public health, create opportunities for art and innovation, contribute to the greater good, and positively impact economic development in Maine and beyond

    Community College Students\u27 Perceptions of Sense of Community and Instructor Presence in the Online Classroom

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    The purpose of this non-experimental, comparative, quantitative study was to determine if there were significant differences between the perceptions of male and female community college students about the importance of sense of community (SoC) in online classes and sense of instructor presence (IP) at eight southern, public, community colleges using survey data. It was the intent of the study to determine if there were significant relationships of students’ perceptions of the presence of sense of community in online classes among factors of age, race, grade point average, cumulative credit hours, credential type, major area of study, and number of previous online courses completed. In addition, possible significant relationships of students’ perceptions of instructor presence in online classes among factors of age, race, grade point average, cumulative credit hours, credential type, major area of study, and number of previous online courses completed were analyzed. The findings provided evidence that for these community college students, demographic characteristics generally did not impact SoC nor student perception of IP. However, students’ open-ended feedback revealed multiple layers of frustration with lack of IP

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Honors Colleges in the 21st Century

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    Table of Contents Acknowledgments Introduction | Richard Badenhausen Part I: Honors College Contexts: Past and Present CHAPTER ONE Oxbridge and Core Curricula: Continuing Conversations with the Past in Honors Colleges | Christopher A. Snyder CHAPTER TWO Characteristics of the 21st-Century Honors College | Andrew J. Cognard-Black and Patricia J. Smith Part II: Transitioning to an Honors College CHAPTER THREE Should We Start an Honors College? An Administrative Playbook for Working Through the Decision | Richard Badenhausen CHAPTER FOUR Beyond the Letterhead: A Tactical Toolbox for Transitioning from Program to College | Sara Hottinger, Megan McIlreavy, Clay Motley, and Louis Keiner Part III: Administrative Leadership CHAPTER FIVE “It Is What You Make It’’: Opportunities Arising from the Unique Roles of Honors College Deans | Jeff Chamberlain, Thomas M. Spencer, and Jefford Vahlbusch CHAPTER SIX The Role of the Honors College Dean in the Future of Honors Education | Peter Parolin, Timothy J. Nichols, Donal C. Skinner, and Rebecca C. Bott-Knutson CHAPTER SEVEN From the Top Down: Implications of Honors College Deans’ Race and Gender | Malin Pereira, Jacqueline Smith-Mason, Karoline Summerville, and Scott Linneman Part IV: Honors College Operations CHAPTER EIGHT Something Borrowed, Something New: Honors College Faculty and the Staffing of Honors Courses | Erin E. Edgington and Linda Frost CHAPTER NINE Telling Your Story: Stewardship and the Honors College | Andrew Martino Part V: Honors Colleges as Leaders in the Work of Diversity, Equity, Inclusion, and Access CHAPTER TEN Cultivating Institutional Change: Infusing Principles of Diversity, Equity, and Inclusion into Everyday Honors College Practices | Tara M. Tuttle, Julie Stewart, and Kayla Powell CHAPTER ELEVEN Positioning Honors Colleges to Lead Diversity and Inclusion Efforts at Predominantly White Institutions | Susan Dinan, Jason T. Hilton, and Jennifer Willford CHAPTER TWELVE Honors Colleges as Levers of Educational Equity | Teagan Decker, Joshua Kalin Busman, and Michele Fazio CHAPTER THIRTEEN Promoting the Inclusion of LGBTQ+ Students: The Role of the Honors College in Faith-Based Colleges and Universities | Paul E. Prill Part VI: Supporting Students CHAPTER FOURTEEN Who Belongs in Honors? Culturally Responsive Advising and Transformative Diversity | Elizabeth Raisanen CHAPTER FIFTEEN Fostering Student Leadership in Honors Colleges | Jill Nelson Granger Part VII: Honors College Curricular Innovation CHAPTER SIXTEEN Honors Liberal Arts for the 21st Century | John Carrell, Aliza S. Wong, Chad Cain, Carrie J. Preston, and Muhammad H. Zaman CHAPTER SEVENTEEN Honors Colleges, Transdisciplinary Education, and Global Challenges | 423 Paul Knox and Paul Heilker Part VIII: Community Engagement CHAPTER EIGHTEEN Teaching and Learning in the Fourth Space: Preparing Scholars to Engage in Solving Community Problems | Heidi Appel, Rebecca C. Bott-Knutson, Joy Hart, Paul Knox, Andrea Radasanu, Leigh E. Fine, Timothy J. Nichols, Daniel Roberts, Keith Garbutt, William Ziegler, Jonathan Kotinek, Kathy Cooke, Ralph Keen, Mark Andersen, and Jyotsna Kapur CHAPTER NINETEEN Serving Our Communities: Leveraging the Honors College Model at Two-Year Institutions | Eric Hoffman, Victoria M. Bryan, and Dan Flores About the Authors About the NCHC Monograph Serie
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