7,362 research outputs found
Intra-procedural Optimization of the Numerical Accuracy of Programs
Numerical programs performing
oating-point computations
are very sensitive to the way formulas are written. These last years, several
techniques have been proposed concerning the transformation of
arithmetic expressions in order to improve their accuracy and, in this article,
we go one step further by automatically transforming larger pieces
of code containing assignments and control structures. We define a set
of transformation rules allowing the generation, under certain conditions
and in polynomial time, of larger expressions by performing limited formal
computations, possibly among several iterations of a loop. These
larger expressions are better suited to improve the numerical accuracy
of the target variable. We use abstract interpretation-based static analysis
techniques to over-approximate the roundoff errors in programs and
during the transformation of expressions. A prototype has been implemented
and experimental results are presented concerning classical numerical
algorithm analysis and algorithm for embedded systems
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Automatic synthesis of analog layout : a survey
A review of recent research in the automatic synthesis of physical geometry for analog integrated circuits is presented. On introduction, an explanation of the difficulties involved in analog layout as opposed to digital layout is covered. Review of the literature then follows. Emphasis is placed on the exposition of general methods for addressing problems specific to analog layout, with the details of specific systems only being given when they surve to illustrate these methods well. The conclusion discusses problems remaining and offers a prediction as to how technology will evolve to solve them. It is argued that although progress has been and will continue to be made in the automation of analog IC layout, due to fundamental differences in the nature of analog IC design as opposed to digital design, it should not be expected that the level of automation of the former will reach that of the latter any time soon
AI at Ames: Artificial Intelligence research and application at NASA Ames Research Center, Moffett Field, California, February 1985
Charts are given that illustrate function versus domain for artificial intelligence (AI) applications and interests and research area versus project number for AI research. A list is given of project titles with associated project numbers and page numbers. Also, project descriptions, including title, participants, and status are given
Mixing Techniques to Compute Derivatives of semi-numerical models: Application to Magnetic Nano Switch Optimization
International audienceThis paper is about derivatives techniques and their composition for semi-numerical models. Techniques such as symbolic derivation and automatic differentiation are addressed. All techniques are illustrated for the gradient based optimization of a magnetic nano switch
A review on integration of artificial intelligence into water quality modelling
2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public health datasets, today, healthcare now generates an incredible amount of
digital information. Such a wealth of data presents an exciting opportunity for
integrated machine learning solutions to address problems across multiple
facets of healthcare practice and administration. Unfortunately, the ability to
derive accurate and informative insights requires more than the ability to
execute machine learning models. Rather, a deeper understanding of the data on
which the models are run is imperative for their success. While a significant
effort has been undertaken to develop models able to process the volume of data
obtained during the analysis of millions of digitalized patient records, it is
important to remember that volume represents only one aspect of the data. In
fact, drawing on data from an increasingly diverse set of sources, healthcare
data presents an incredibly complex set of attributes that must be accounted
for throughout the machine learning pipeline. This chapter focuses on
highlighting such challenges, and is broken down into three distinct
components, each representing a phase of the pipeline. We begin with attributes
of the data accounted for during preprocessing, then move to considerations
during model building, and end with challenges to the interpretation of model
output. For each component, we present a discussion around data as it relates
to the healthcare domain and offer insight into the challenges each may impose
on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20
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