3,323 research outputs found
Structural Analysis: Shape Information via Points-To Computation
This paper introduces a new hybrid memory analysis, Structural Analysis,
which combines an expressive shape analysis style abstract domain with
efficient and simple points-to style transfer functions. Using data from
empirical studies on the runtime heap structures and the programmatic idioms
used in modern object-oriented languages we construct a heap analysis with the
following characteristics: (1) it can express a rich set of structural, shape,
and sharing properties which are not provided by a classic points-to analysis
and that are useful for optimization and error detection applications (2) it
uses efficient, weakly-updating, set-based transfer functions which enable the
analysis to be more robust and scalable than a shape analysis and (3) it can be
used as the basis for a scalable interprocedural analysis that produces precise
results in practice.
The analysis has been implemented for .Net bytecode and using this
implementation we evaluate both the runtime cost and the precision of the
results on a number of well known benchmarks and real world programs. Our
experimental evaluations show that the domain defined in this paper is capable
of precisely expressing the majority of the connectivity, shape, and sharing
properties that occur in practice and, despite the use of weak updates, the
static analysis is able to precisely approximate the ideal results. The
analysis is capable of analyzing large real-world programs (over 30K bytecodes)
in less than 65 seconds and using less than 130MB of memory. In summary this
work presents a new type of memory analysis that advances the state of the art
with respect to expressive power, precision, and scalability and represents a
new area of study on the relationships between and combination of concepts from
shape and points-to analyses
Stop smoking the Easyway:addiction, self-help and tobacco cessation
This article examines Easyway, a popular clinical and self-help method for the treatment of smoking addiction established by the late Allen Carr in 1984. It begins by addressing how smoking has come to be constituted as a neuropharmacological addiction and some of the issues and concerns raised against this in the social sciences. After situating its theoretical and empirical focus, the article then proceeds with an interpretative thematic analysis of a selection of Easyway self-help texts. The aims here are as follows: firstly, to show how Easyway, as a discourse, constitutes the problem of nicotine addiction in novel and distinctive ways; secondly, to elaborate how the Easyway texts seek to govern readers – paradoxically, through their free capacity for reflection, introspection and action – to overcome their situated addiction to smoking; and thirdly, to identify and locate the significance of the author’s implicit claims to charisma in underpinning his authority to know and treat nicotine addiction
Review of: M. Stuart Madden, Toxic Torts Deskbook
M. Stuart Madden, Toxic Torts Deskbook (Lewis Publishers 1992). Acknowledgements, case index, general index, notes, preface. LC 91-48238; ISBN 0- 87371-508-X. [230 pp. Cloth 84.00 elsewhere. 2000 Corporate Boulevard, NW, Boca Raton FL 33431.]Review of
Principal Nested Spheres for Time Warped Functional Data Analysis
There are often two important types of variation in functional data: the
horizontal (or phase) variation and the vertical (or amplitude) variation.
These two types of variation have been appropriately separated and modeled
through a domain warping method (or curve registration) based on the Fisher Rao
metric. This paper focuses on the analysis of the horizontal variation,
captured by the domain warping functions. The square-root velocity function
representation transforms the manifold of the warping functions to a Hilbert
sphere. Motivated by recent results on manifold analogs of principal component
analysis, we propose to analyze the horizontal variation via a Principal Nested
Spheres approach. Compared with earlier approaches, such as approximating
tangent plane principal component analysis, this is seen to be the most
efficient and interpretable approach to decompose the horizontal variation in
some examples
A scale-based approach to finding effective dimensionality in manifold learning
The discovering of low-dimensional manifolds in high-dimensional data is one
of the main goals in manifold learning. We propose a new approach to identify
the effective dimension (intrinsic dimension) of low-dimensional manifolds. The
scale space viewpoint is the key to our approach enabling us to meet the
challenge of noisy data. Our approach finds the effective dimensionality of the
data over all scale without any prior knowledge. It has better performance
compared with other methods especially in the presence of relatively large
noise and is computationally efficient.Comment: Published in at http://dx.doi.org/10.1214/07-EJS137 the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
PCA consistency in high dimension, low sample size context
Principal Component Analysis (PCA) is an important tool of dimension
reduction especially when the dimension (or the number of variables) is very
high. Asymptotic studies where the sample size is fixed, and the dimension
grows [i.e., High Dimension, Low Sample Size (HDLSS)] are becoming increasingly
relevant. We investigate the asymptotic behavior of the Principal Component
(PC) directions. HDLSS asymptotics are used to study consistency, strong
inconsistency and subspace consistency. We show that if the first few
eigenvalues of a population covariance matrix are large enough compared to the
others, then the corresponding estimated PC directions are consistent or
converge to the appropriate subspace (subspace consistency) and most other PC
directions are strongly inconsistent. Broad sets of sufficient conditions for
each of these cases are specified and the main theorem gives a catalogue of
possible combinations. In preparation for these results, we show that the
geometric representation of HDLSS data holds under general conditions, which
includes a -mixing condition and a broad range of sphericity measures of
the covariance matrix.Comment: Published in at http://dx.doi.org/10.1214/09-AOS709 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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