73 research outputs found

    A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

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    Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types

    ATLAS detector and physics performance: Technical Design Report, 1

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    The Sample Analysis at Mars Investigation and Instrument Suite

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    Routing-Aware Application Mapping Considering Steiner Points for Coarse-Grained Reconfigurable Architecture

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    Datapath-oriented FPGA Mapping and Placement for Configurable Computing (Extended Abstract)

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    Timothy J. Callahan and John Wawrzynek University of California--Berkeley Widespread acceptance of FPGA-based reconfigurable coprocessors will be expedited if compilation time for FPGA configurations can be reduced to be comparable to software compilation. This research achieves this goal, generating complete datapath layouts in fractions of a second rather than hours. Our algorithm, adapted from instruction selection in compilers, packs multiple operations into single rows of CLBs when possible, while preserving a regular bit-slice layout. Furthermore, placement and thus routing delays are considered simultaneously with packing, so that the total delay, not just the CLB delay, is optimized. The Problem Reconfigurable coprocessors, most commonly implemented with field programmable gate array (FPGA) technology, have been shown effective in accelerating certain classes of applications. Computation-intense kernels can be selected automatically or by hand for acceleration using the coproc..

    Performance Gains from Partitioning Embedded Applications in Processor-FPGA SoCs

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    Adaptive Computing Systems and their Design Tools

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    While reconfigurable adaptive computing has many proven advantages over conventional processors, in practice, it is often limited to niche applications. This situation, which we aim to resolve with our research, is often linked to the lack of programming languages for adaptive computers that are familiar to software developers. We present a compile flow capable of translating general-purpose C programs to hybrid hardware/software applications for execution on an adaptive computer and give an overview of the required advances in compiler technology as well as in computer architecture and operating system design
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