611 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Honors Colleges in the 21st Century
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
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Advanced Sensing, Fault Diagnostics, and Structural Health Management
Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes
Implementation and Synthesis of Math Library Functions
Achieving speed and accuracy for math library functions like exp, sin, and
log is difficult. This is because low-level implementation languages like C do
not help math library developers catch mathematical errors, build
implementations incrementally, or separate high-level and low-level decision
making. This ultimately puts development of such functions out of reach for all
but the most experienced experts. To address this, we introduce MegaLibm, a
domain-specific language for implementing, testing, and tuning math library
implementations. MegaLibm is safe, modular, and tunable. Implementations in
MegaLibm can automatically detect mathematical mistakes like sign flips via
semantic wellformedness checks, and components like range reductions can be
implemented in a modular, composable way, simplifying implementations. Once the
high-level algorithm is done, tuning parameters like working precisions and
evaluation schemes can be adjusted through orthogonal tuning parameters to
achieve the desired speed and accuracy. MegaLibm also enables math library
developers to work interactively, compiling, testing, and tuning their
implementations and invoking tools like Sollya and type-directed synthesis to
complete components and synthesize entire implementations. MegaLibm can express
8 state-of-the-art math library implementations with comparable speed and
accuracy to the original C code, and can synthesize 5 variations and 3
from-scratch implementations with minimal guidance.Comment: 25 pages, 12 figure
Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design
Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data
Teaching and Collecting Technical Standards: A Handbook for Librarians and Educators
Technical standards are a vital source of information for providing guidelines during the design, manufacture, testing, and use of whole products, materials, and components. To prepare students—especially engineering students—for the workforce, universities are increasing the use of standards within the curriculum. Employers believe it is important for recent university graduates to be familiar with standards. Despite the critical role standards play within academia and the workforce, little information is available on the development of standards information literacy, which includes the ability to understand the standardization process; identify types of standards; and locate, evaluate, and use standards effectively.
Libraries and librarians are a critical part of standards education, and much of the discussion has been focused on the curation of standards within libraries. However, librarians also have substantial experience in developing and teaching standards information literacy curriculum. With the need for universities to develop a workforce that is well-educated on the use of standards, librarians and course instructors can apply their experiences in information literacy toward teaching students the knowledge and skills regarding standards that they will need to be successful in their field. This title provides background information for librarians on technical standards as well as collection development best practices. It also creates a model for librarians and course instructors to use when building a standards information literacy curriculum.https://docs.lib.purdue.edu/pilh/1004/thumbnail.jp
Efficient Path Enumeration and Structural Clustering on Massive Graphs
Graph analysis plays a crucial role in understanding the relationships and structures within complex systems. This thesis focuses on addressing fundamental problems in graph analysis, including hop-constrained s-t simple path (HC-s-t path) enumeration, batch HC-s-t path query processing, and graph structural clustering (SCAN). The objective is to develop efficient and scalable distributed algorithms to tackle these challenges, particularly in the context of billion-scale graphs.
We first explore the problem of HC-s-t path enumeration. Existing solutions for this problem often suffer from inefficiency and scalability limitations, especially when dealing with billion-scale graphs. To overcome these drawbacks, we propose a novel hybrid search paradigm specifically tailored for HC-s-t path enumeration. This paradigm combines different search strategies to effectively explore the solution space. Building upon this paradigm, we devise a distributed enumeration algorithm that follows a divide-and-conquer strategy, incorporates fruitless exploration pruning, and optimizes memory consumption. Experimental evaluations on various datasets demonstrate that our algorithm achieves a significant speedup compared to existing solutions, even on datasets where they encounter out-of-memory issues.
Secondly, we address the problem of batch HC-s-t path query processing. In real-world scenarios, it is common to issue multiple HC-s-t path queries simultaneously and process them as a batch. However, existing solutions often focus on optimizing the processing performance of individual queries, disregarding the benefits of processing queries concurrently. To bridge this gap, we propose the concept of HC-s path queries, which captures the common computation among different queries. We design a two-phase HC-s path query detection algorithm to identify the shared computation for a given set of HC-s-t path queries. Based on the detected HC-s path queries, we develop an efficient HC-s-t path enumeration algorithm that effectively shares the common computation. Extensive experiments on diverse datasets validate the efficiency and scalability of our algorithm for processing multiple HC-s-t path queries concurrently.
Thirdly, we investigate the problem of graph structural clustering (SCAN) in billion-scale graphs. Existing distributed solutions for SCAN often lack efficiency or suffer from high memory consumption, making them impractical for large-scale graphs. To overcome these challenges, we propose a fine-grained clustering framework specifically tailored for SCAN. This framework enables effective identification of cohesive subgroups within a graph. Building upon this framework, we devise a distributed SCAN algorithm that minimizes communication overhead and reduces memory consumption throughout the execution. We also incorporate an effective workload balance mechanism that dynamically adjusts to handle skewed workloads. Experimental evaluations on real-world graphs demonstrate the efficiency and scalability of our proposed algorithm.
Overall, this thesis contributes novel distributed algorithms for HC-s-t path enumeration, batch HC-s-t path query processing, and graph structural clustering. The proposed algorithms address the efficiency and scalability challenges in graph analysis, particularly on billion-scale graphs. Extensive experimental evaluations validate the superiority of our algorithms compared to existing solutions, enabling efficient and scalable graph analysis in complex systems
OCCL: a Deadlock-free Library for GPU Collective Communication
Various distributed deep neural network (DNN) training technologies lead to
increasingly complicated use of collective communications on GPU. The
deadlock-prone collectives on GPU force researchers to guarantee that
collectives are enqueued in a consistent order on each GPU to prevent
deadlocks. In complex distributed DNN training scenarios, manual hardcoding is
the only practical way for deadlock prevention, which poses significant
challenges to the development of artificial intelligence. This paper presents
OCCL, which is, to the best of our knowledge, the first deadlock-free
collective communication library for GPU supporting dynamic decentralized
preemption and gang-scheduling for collectives. Leveraging the preemption
opportunity of collectives on GPU, OCCL dynamically preempts collectives in a
decentralized way via the deadlock-free collective execution framework and
allows dynamic decentralized gang-scheduling via the stickiness adjustment
scheme. With the help of OCCL, researchers no longer have to struggle to get
all GPUs to launch collectives in a consistent order to prevent deadlocks. We
implement OCCL with several optimizations and integrate OCCL with a distributed
deep learning framework OneFlow. Experimental results demonstrate that OCCL
achieves comparable or better latency and bandwidth for collectives compared to
NCCL, the state-of-the-art. When used in distributed DNN training, OCCL can
improve the peak training throughput by up to 78% compared to statically
sequenced NCCL, while introducing overheads of less than 6.5% across various
distributed DNN training approaches
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