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Quantifying the multi-level nature of tiling interactions 10th

10th Annual International Workshop on Languages and Compilers for Parallel Computing, LCPC 1997

作者： Mitchell, Nicholas Carter, Larry Ferrante, Jeanne Högstedt, Karin Computer Science and Engineering Department UCSD La JollaCA92093-0114 United States

ISBN: (纸本)3540644725

Optimizations, including tiling, often target a single level of memory or parallelism, such as cache. These optimizations usually operate on a level-by-level basis, guided by a cost function parameterized by features of that single level. The benefit of optimizations guided by these one-level cost functions decreases as architectures tend towards a hierarchy of memory and of parallelism. We have identified three common architectural scenarios where a single tiling choice could be improved by using information from multiple levels in concert. For the first two scenarios, we derive multi-level cost functions which guide the optimal choice of tile size and shape, and quantify the improvement gained. We give both analysis and simulation results to support our points. For the third scenario, we summarize our findings. © 1998, Springer-Verlag. All rights reserved.

关键词： Cost functions

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Evolving Compare-Exchange Networks Using Grammars

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Artificial Life 1995年第2期2卷 199-237页

作者： Thomas E. Kammeyer Richard K. Belew S. Gill Williamson Cognitive Computer Science Research Group Computer Science & Engineering Department (0114) University of California at San Diego La Jolla CA 92007-0114. tkammeye@cs.ucsd.edu Cognitive Computer Science Research Group Computer Science & Engineering Department (0114) University of California at San Diego La Jolla CA 92007-0114.

We apply a genetic algorithm whose genotypes represent simple grammars to search for families of compare-exchange networks that are merging networks. The grammars are restricted to be of a simple form based on prior knowledge about the search domain. Finding merging networks in this fashion leads to the discovery of networks that also sort if the data is cycled through them a small number of times. Random network and grammar generation results show that the problem is difficult and suggest some conjectures about how the genetic algorithm is operating for this problem. The genetic algorithm finds the best-known network of the kind for which we search, and further finds a novel, slightly suboptimal network that turns out to be an interesting combination of ideas from known, theoretically derived networks.

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Motion planning on a graph (extended abstract)

Motion planning on a graph (extended abstract)

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Proceedings of the 35th IEEE Annual Symposium on Foundations of computer science

作者： Papadimitriou, Christos H. Raghavan, Prabhakar Sudan, Madhu Tamaki, Hisao Department of Computer Science and Engineering UCSD San DiegoCA92093 United States IBM T.J. Watson Research Center Yorktown HeightsNY10598 United States IBM Tokyo Research Laboratory Kanagawa242 Japan The IBM T.J. Watson Research Center

We are given a connected, undirected graph G on n vertices. There is a mobile robot on one of the vertices;this vertex is labeled 5. Each of several other vertices contains a single movable obstacle. The robot and the obstacles may only reside at vertices, although they may be moved across edges. A vertex may never contain more than one object (robot/obstacle). In one step, we may move either the robot or one of the obstacles from its current position v to a vacant vertex adjacent to v. Our goal is to move the robot to a designated vertex t using the smallest number of steps possible. The problem is a simple abstraction of a robot motion planning problem, with the geometry replaced by the adjacencies in the graph. We point out its connections to robot motion planning. We study its complexity, giving exact and approximate algorithms for several cases. © 1994 IEEE

关键词： Motion planning

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Competitive implementation of parallel programs 93

Competitive implementation of parallel programs

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Proceedings of the fourth annual ACM-SIAM symposium on Discrete algorithms

作者： Xiaotie Deng Elias Koutsoupias Department of Computer Science York University North York Ontario M3J 1P3 Department of Computer Science and Engineering UCSD Jolla CA

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How Creative Practitioners Use Tools to Capture Ideas: A Cross-Domain Study

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ACM Transactions on computer-Human Interaction 1000年

作者： Emilia Rosselli Del Turco Nanna Inie James D. Hollan Peter Dalsgaard University of California San Diego Department of Cognitive Science and UCSD Design Lab USA IT University of Copenhagen Computer Science Denmark and University of Washington Paul G. Allen School of Computer Science & Engineering USA Centre for Digital Creativity University of Aarhus Denmark

Creative practitioners rely on tools to capture and manage ideas as a foundational aspect of their work. However, we have little knowledge about how idea management practices vary in different creative domains. Combining insights from qualitative surveys (\(N\) \(\sim\)200) and follow-up in-depth interviews (\(n\) \(\sim\)60) with creative professionals from four domains (interaction design, research, music, and graphics) of creative work, we report on (1) how ideas are externalized in practitioners’ archives, (2) what they consider important when choosing tools to capture ideas, and (3) how these tool collections resemble and differ from each other. Our cross-domain study demonstrates that participants’ tool use reflects idea capture characteristic needs as well as domain-specific views about the creative process. We conclude with a discussion about capturing as an externalizing activity, practitioners’ use of the term ideas, and four suggestions for directions in the design of creativity support tools.

关键词： Idea Management Creativity Creativity Support Tools Innovation Knowledge Management

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Methods for Robot Behavior Adaptation for Cognitive Neurorehabilitation

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Control, Robotics, and Autonomous Systems 1000年第1期5卷 109-135页

作者： Alyssa Kubota Laurel D. Riek Department of Computer Science and Engineering University of California San Diego La Jolla California USA email: akubota@ucsd.edulriek@ucsd.edu

An estimated 11% of adults report experiencing some form of cognitive decline, which may be associated with conditions such as stroke or dementia and can impact their memory, cognition, behavior, and physical abilities. While there are no known pharmacological treatments for many of these conditions, behavioral treatments such as cognitive training can prolong the independence of people with cognitive impairments. These treatments teach metacognitive strategies to compensate for memory difficulties in their everyday lives. Personalizing these treatments to suit the preferences and goals of an individual is critical to improving their engagement and sustainment, as well as maximizing the treatment's effectiveness. Robots have great potential to facilitate these training regimens and support people with cognitive impairments, their caregivers, and clinicians. This article examines how robots can adapt their behavior to be personalized to an individual in the context of cognitive neurorehabilitation. We provide an overview of existing robots being used to support neurorehabilitation and identify key principles for working in this space. We then examine state-of-the-art technical approaches for enabling longitudinal behavioral adaptation. To conclude, we discuss our recent work on enabling social robots to automatically adapt their behavior and explore open challenges for longitudinal behavior adaptation. This work will help guide the robotics community as it continues to provide more engaging, effective, and personalized interactions between people and robots.

关键词： human–robot interaction behavior adaptation neurorehabilitation healthcare robotics cognitive robotics social robotics

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Extrachromosomal DNA in Cancer

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Genomics and Human Genetics 1000年第1期23卷 29-52页

作者： Vineet Bafna Paul S. Mischel 1Department of Computer Science and Engineering and Halıcıoğlu Data Science Institute University of California San Diego La Jolla California USA email: vbafna@cs.ucsd.edu 2Department of Pathology and ChEM-H Stanford University School of Medicine Stanford California USA email: pmischel@stanford.edu

In cancer, complex genome rearrangements and other structural alterations, including the amplification of oncogenes on circular extrachromosomal DNA (ecDNA) elements, drive the formation and progression of tumors. ecDNA is a particularly challenging structural alteration. By untethering oncogenes from chromosomal constraints, it elevates oncogene copy number, drives intratumoral genetic heterogeneity, promotes rapid tumor evolution, and results in treatment resistance. The profound changes in DNA shape and nuclear architecture generated by ecDNA alter the transcriptional landscape of tumors by catalyzing new types of regulatory interactions that do not occur on chromosomes. The current suite of tools for interrogating cancer genomes is well suited for deciphering sequence but has limited ability to resolve the complex changes in DNA structure and dynamics that ecDNA generates. Here, we review the challenges of resolving ecDNA form and function and discuss the emerging tool kit for deciphering ecDNA architecture and spatial organization, including what has been learned to date about how this dramatic change in shape alters tumor development, progression, and drug resistance.

关键词： ecDNA cancer homogeneously staining regions HSRs ecDNA evolution ecDNA hubs enhancer hijacking

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Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

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Biomedical Data science 1000年第1期7卷 369-389页

作者： Anthony Cesnik Leah V. Schaffer Ishan Gaur Mayank Jain Trey Ideker Emma Lundberg 1Department of Bioengineering Stanford University Stanford California USA email: emmalu@stanford.edu 2Department of Medicine University of California San Diego La Jolla California USA email: tideker@health.ucsd.edu 3Departments of Computer Science and Engineering and Bioengineering University of California San Diego La Jolla California USA 4Department of Pathology Stanford University Palo Alto California USA 5Science for Life Laboratory School of Engineering Sciences in Chemistry Biotechnology and Health KTH Royal Institute of Technology Stockholm Sweden 6Chan Zuckerberg Biohub San Francisco California USA

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

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Multispecies Coalescent: Theory and Applications in Phylogenetics

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Ecology, Evolution, and Systematics 1000年第1期52卷 247-268页

作者： Siavash Mirarab Luay Nakhleh Tandy Warnow 1Electrical and Computer Engineering Department University of California San Diego La Jolla California 92093 USA email: smirarab@ucsd.edu 2Department of Computer Science Rice University Houston Texas 77005 USA email: nakhleh@rice.edu 3Department of Computer Science University of Illinois Urbana-Champaign Urbana Illinois 61801 USA email: warnow@illinois.edu

Species tree estimation is a basic part of many biological research projects, ranging from answering basic evolutionary questions (e.g., how did a group of species adapt to their environments?) to addressing questions in functional biology. Yet, species tree estimation is very challenging, due to processes such as incomplete lineage sorting, gene duplication and loss, horizontal gene transfer, and hybridization, which can make gene trees differ from each other and from the overall evolutionary history of the species. Over the last 10–20 years, there has been tremendous growth in methods and mathematical theory for estimating species trees and phylogenetic networks, and some of these methods are now in wide use. In this survey, we provide an overview of the current state of the art, identify the limitations of existing methods and theory, and propose additional research problems and directions.

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The Microbiome and Human Biology

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Genomics and Human Genetics 1000年第1期18卷 65-86页

作者： Rob Knight Chris Callewaert Clarisse Marotz Embriette R. Hyde Justine W. Debelius Daniel McDonald Mitchell L. Sogin 3Center for Microbiome Innovation University of California San Diego La Jolla California 92093 2Department of Computer Science and Engineering University of California San Diego La Jolla California 92093 1Department of Pediatrics University of California San Diego La Jolla California 92093 email: robknight@ucsd.edu 4Center for Microbial Ecology and Technology Ghent University 9000 Ghent Belgium 5Josephine Bay Paul Center Marine Biological Laboratory Woods Hole Massachusetts 02543

Over the past few years, microbiome research has dramatically reshaped our understanding of human biology. New insights range from an enhanced understanding of how microbes mediate digestion and disease processes (e.g., in inflammatory bowel disease) to surprising associations with Parkinson's disease, autism, and depression. In this review, we describe how new generations of sequencing technology, analytical advances coupled to new software capabilities, and the integration of animal model data have led to these new discoveries. We also discuss the prospects for integrating studies of the microbiome, metabolome, and immune system, with the goal of elucidating mechanisms that govern their interactions. This systems-level understanding will change how we think about ourselves as organisms.

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