144,972 research outputs found
An Interactive Approach for Identifying Structure Definitions
Our ability to grasp and understand complex phenomena is essentially based on recognizing
structures and relating these to each other. For example, any meteorological description of
a weather condition and explanation of its evolution recurs to meteorological structures,
such as convection and circulation structures, cloud fields and rain fronts. All of these
are spatiotemporal structures, defined by time-dependent patterns in the underlying fields.
Typically, such a structure is defined by a verbal description that corresponds to the more or
less uniform, often somewhat vague mental images of the experts.
However, a precise, formal definition of the structures or, more generally, concepts is often
desirable, e.g., to enable automated data analysis or the development of phenomenological
models. Here, we present a systematic approach and an interactive tool to obtain formal
definitions of spatiotemporal structures. The tool enables experts to evaluate and compare
different structure definitions on the basis of data sets with time-dependent fields that
contain the respective structure. Since structure definitions are typically parameterized, an
essential part is to identify parameter ranges that lead to desired structures in all time steps.
In addition, it is important to allow a quantitative assessment of the resulting structures
simultaneously. We demonstrate the use of the tool by applying it to two meteorological
examples: finding structure definitions for vortex cores and center lines of temporarily
evolving tropical cyclones.
Ideally, structure definitions should be objective and applicable to as many data sets as
possible. However, finding such definitions, e.g., for the common atmospheric structures
in meteorology, can only be a long-term goal. The proposed procedure, together with the
presented tool, is just a first systematic approach aiming at facilitating this long and arduous
way.
Keywords: Visual data analysis; Coherent and persistent structures; Atmospheric vortices;
Tropical storms;
An efficient, parametric fixpoint algorithm for analysis of java bytecode
Abstract interpretation has been widely used for the analysis of object-oriented languages and, in particular, Java source and bytecode. However, while most existing work deals with the problem of flnding expressive abstract domains that track accurately the characteristics of a particular concrete property, the underlying flxpoint algorithms have received comparatively less attention. In fact, many existing (abstract interpretation based—) flxpoint algorithms rely on relatively inefHcient techniques for solving inter-procedural caligraphs or are speciflc and tied to particular analyses. We also argüe that the design of an efficient fixpoint algorithm is pivotal to supporting the analysis of large programs. In this paper we introduce a novel algorithm for analysis of Java bytecode which includes a number of optimizations in order to reduce the number of iterations. The algorithm is parametric -in the sense that it is independent of the abstract domain used and it can be applied to different domains as "plug-ins"-, multivariant, and flow-sensitive. Also, is based on a program transformation, prior to the analysis, that results in a highly uniform representation of all the features in the language and therefore simplifies analysis. Detailed descriptions of decompilation solutions are given and discussed with an example. We also provide some performance data from a preliminary implementation of the analysis
Image Characterization and Classification by Physical Complexity
We present a method for estimating the complexity of an image based on
Bennett's concept of logical depth. Bennett identified logical depth as the
appropriate measure of organized complexity, and hence as being better suited
to the evaluation of the complexity of objects in the physical world. Its use
results in a different, and in some sense a finer characterization than is
obtained through the application of the concept of Kolmogorov complexity alone.
We use this measure to classify images by their information content. The method
provides a means for classifying and evaluating the complexity of objects by
way of their visual representations. To the authors' knowledge, the method and
application inspired by the concept of logical depth presented herein are being
proposed and implemented for the first time.Comment: 30 pages, 21 figure
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