9,852 research outputs found
Dogmatism and Theoretical Pluralism in Modern Cosmology
This work discusses the presence of a dogmatic tendency within modern
cosmology, and some ideas capable of neutralizing its negative influence. It is
verified that warnings about the dangers of dogmatic thinking in cosmology can
be found as early as the 1930's, and we discuss the modern appearance of
"scientific dogmatism". The solution proposed to counteract such an influence,
which is capable of neutralizing this dogmatic tendency, has its origins in the
philosophical thinking of the Austrian physicist Ludwig Boltzmann (1844-1906).
In particular we use his two main epistemological theses, scientific theories
as representations of nature and theoretical pluralism, to show that once they
are embodied in the research practice of modern cosmology, there is no longer
any reason for dogmatic behaviours.Comment: 14 pages; LaTeX sourc
Boltzmann's Concept of Reality
In this article we describe and analyze the concept of reality developed by
the Austrian theoretical physicist Ludwig Boltzmann. It is our thesis that
Boltzmann was fully aware that reality could, and actually was, described by
different points of view. In spite of this, Boltzmann did not renounce the idea
that reality is real. We also discuss his main motivations to be strongly
involved with philosophy of science, as well as further developments made by
Boltzmann himself of his main philosophical ideas, namely scientific theories
as images of Nature and its consequences. We end the paper with a discussion
about the modernity of Boltzmann's philosophy of science.Comment: 13 pages, pdf only. To appear in the book on Ludwig Boltzmann
scientific philosophy, published by Nova Science. Edited by A. Eftekhar
Convolutional Neural Networks Via Node-Varying Graph Filters
Convolutional neural networks (CNNs) are being applied to an increasing
number of problems and fields due to their superior performance in
classification and regression tasks. Since two of the key operations that CNNs
implement are convolution and pooling, this type of networks is implicitly
designed to act on data described by regular structures such as images.
Motivated by the recent interest in processing signals defined in irregular
domains, we advocate a CNN architecture that operates on signals supported on
graphs. The proposed design replaces the classical convolution not with a
node-invariant graph filter (GF), which is the natural generalization of
convolution to graph domains, but with a node-varying GF. This filter extracts
different local features without increasing the output dimension of each layer
and, as a result, bypasses the need for a pooling stage while involving only
local operations. A second contribution is to replace the node-varying GF with
a hybrid node-varying GF, which is a new type of GF introduced in this paper.
While the alternative architecture can still be run locally without requiring a
pooling stage, the number of trainable parameters is smaller and can be
rendered independent of the data dimension. Tests are run on a synthetic source
localization problem and on the 20NEWS dataset.Comment: Submitted to DSW 2018 (IEEE Data Science Workshop
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