38,965 research outputs found
Spatial Aggregation: Theory and Applications
Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computations around image-like, analogue representations so that
perceptual and symbolic operations can be brought to bear to infer structure
and behavior. Programs incorporating imagistic reasoning have been shown to
perform at an expert level in domains that defy current analytic or numerical
methods. We have developed a computational paradigm, spatial aggregation, to
unify the description of a class of imagistic problem solvers. A program
written in this paradigm has the following properties. It takes a continuous
field and optional objective functions as input, and produces high-level
descriptions of structure, behavior, or control actions. It computes a
multi-layer of intermediate representations, called spatial aggregates, by
forming equivalence classes and adjacency relations. It employs a small set of
generic operators such as aggregation, classification, and localization to
perform bidirectional mapping between the information-rich field and
successively more abstract spatial aggregates. It uses a data structure, the
neighborhood graph, as a common interface to modularize computations. To
illustrate our theory, we describe the computational structure of three
implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the
spatial aggregation generic operators by mixing and matching a library of
commonly used routines.Comment: See http://www.jair.org/ for any accompanying file
Characteristic exponents of complex networks
We present a novel way to characterize the structure of complex networks by
studying the statistical properties of the trajectories of random walks over
them. We consider time series corresponding to different properties of the
nodes visited by the walkers. We show that the analysis of the fluctuations of
these time series allows to define a set of characteristic exponents which
capture the local and global organization of a network. This approach provides
a way of solving two classical problems in network science, namely the
systematic classification of networks, and the identification of the salient
properties of growing networks. The results contribute to the construction of a
unifying framework for the investigation of the structure and dynamics of
complex systems.Comment: 6 pages, 5 figures, 1 tabl
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
Four Degrees of Separation, Really
We recently measured the average distance of users in the Facebook graph,
spurring comments in the scientific community as well as in the general press
("Four Degrees of Separation"). A number of interesting criticisms have been
made about the meaningfulness, methods and consequences of the experiment we
performed. In this paper we want to discuss some methodological aspects that we
deem important to underline in the form of answers to the questions we have
read in newspapers, magazines, blogs, or heard from colleagues. We indulge in
some reflections on the actual meaning of "average distance" and make a number
of side observations showing that, yes, 3.74 "degrees of separation" are really
few
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